Author: Bernard Bel

Bernard Bel

Journées d'Informatique Musicale 1996, Caen (France)

Abstract

Most com­put­er music envi­ron­ments priv­i­lege music rep­re­sen­ta­tions derived from west­ern com­mon music nota­tion, which make it dif­fi­cult to explore musi­cal ideas based on dif­fer­ent con­cepts. This is notably the case with Indian com­posers eager to han­dle sophis­ti­cat­ed note treat­ment and com­plex polyrhyth­mic struc­tures. This paper presents recent devel­op­ments of a com­po­si­tion­al envi­ron­ment, Bol Processor BP2, address­ing the issue of abstract and com­pre­hen­sive, alto­geth­er flex­i­ble and accu­rate, music representations.

Sound examples

Broken links point to sound exam­ples, which can be found in this fold­er. Just replace 'mov' exten­sions with 'mp3'.

Download this paper

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Seven radical ideas from an old music software that are still revolutionary today

This blog post was cre­at­ed with the help of NotebookLM (November 2025)

👉 In the fol­low­ing text, "BP2" is the old acronym for "Bol Processor". It can be replaced with "BP3".

Introduction: The Unseen Blueprint of Digital Music

Open any mod­ern music pro­duc­tion app, and you’re greet­ed by a famil­iar land­scape: a piano roll, a time­line divid­ed into bars and beats, and tools designed to place dis­crete notes on a grid. This envi­ron­ment is so uni­ver­sal that we rarely ques­tion its foun­da­tions. We treat it as a neu­tral can­vas for creativity.

But what if it isn’t? What if the very archi­tec­ture of our dig­i­tal tools—the twelve-tone scale, the rigid tim­ing, the con­cept of a sin­gle "note"—is a cul­tur­al box, sub­tly shap­ing our music to fit a Western mod­el? For musi­cians from tra­di­tions with dif­fer­ent melod­ic and rhyth­mic philoso­phies, this isn't just a hypo­thet­i­cal ques­tion. It's a dai­ly cre­ative hur­dle. A fas­ci­nat­ing piece of 1990s soft­ware called Bol Processor BP2 offered a rad­i­cal­ly dif­fer­ent path, and its core con­cepts are more rel­e­vant today than ever. This is a look at the most mind-bending ideas from a pro­gram that dared to think out­side the grid.

1. Our Digital Tools Have a Western Accent

The cen­tral prob­lem that Bol Processor BP2 set out to solve is that most music soft­ware is built on a foun­da­tion of Western com­mon music nota­tion. It priv­i­leges the twelve-tone sys­tem and the bina­ry divi­sion of time (whole notes, half notes, quar­ter notes, etc.). This isn't just about the on-screen inter­face; as the source text notes, the MIDI stan­dard itself helps "rein­force this con­ven­tion­al approach," mak­ing it a sys­temic bias built into the very plumb­ing of dig­i­tal music. This design choice cre­ates an imme­di­ate and pro­found chal­lenge for com­posers from oth­er musi­cal worlds, such as India, who want to express the "melod­ic sub­tleties and rhyth­mic intri­ca­cies of their musi­cal heritage."

When the tools don't match the tra­di­tion, the music itself is com­pro­mised. In a com­mer­cial set­ting, this can lead to a prob­lem­at­ic for­mu­la where tra­di­tion­al instru­ments are reduced to a mere "masala ingre­di­ent" sprin­kled on top of a gener­ic elec­tron­ic track. The expres­sive depth of the orig­i­nal music is lost because the under­ly­ing dig­i­tal skele­ton can't sup­port it. This isn't a fail­ure of the musi­cian, but a lim­i­ta­tion of the tool. A new approach wasn't just a tech­ni­cal chal­lenge; it was a cul­tur­al necessity.

2. The Big Idea: Treating Music Like Vector Graphics, Not Pixels

To break free from the Western frame­work, BP2’s cre­ators re-imagined the very nature of musi­cal data. They drew a dis­tinc­tion between "descrip­tive" and "pre­scrip­tive" nota­tion, using a pow­er­ful anal­o­gy from graph­ic design.

• Descriptive nota­tion is like a "pix­el" image. To cre­ate a curve, you must explic­it­ly define the col­or and posi­tion of every sin­gle pix­el along its path. In music, this is like using a stan­dard sequencer to cre­ate a del­i­cate Indian melod­ic pat­tern (gama­ka), where the com­pos­er must man­u­al­ly pro­gram every sin­gle micro­ton­al "PitchBend" mes­sage. It’s clum­sy, com­pu­ta­tion­al­ly inef­fi­cient, and fails to cap­ture the holis­tic ges­ture of the musi­cal idea.
• BP2's approach is like "vec­tor" graph­ics. Instead of defin­ing pix­els, a vector-based pro­gram uses math­e­mat­i­cal descriptions—a start­ing point, an end­ing point, and the shape of the curve in between. BP2 applies this log­ic to music. It uses abstract, effi­cient rep­re­sen­ta­tions to gen­er­ate com­plex and accu­rate musi­cal shapes with­out need­ing to define every sin­gle point.

This shift is more than just an effi­cien­cy hack; it's a philo­soph­i­cal one. A vec­tor encodes the composer's intent—the idea of a gesture—while pix­els mere­ly describe a sta­t­ic, unchange­able result. This con­cep­tu­al leap is the key to BP2's pow­er, offer­ing immense cre­ative free­dom and efficiency.

3. Beyond the "Note": Learning from Indian Drum Syllables

In Western music, the "note" is the fun­da­men­tal build­ing block. But BP2 pro­posed a more flex­i­ble and musi­cal­ly intu­itive unit: the "sound-object." This con­cept was derived direct­ly from the study of North Indian tabla drum­ming, where ono­matopoe­ic syl­la­bles called bols are used to rep­re­sent com­plex rhyth­mic gestures.

The cre­ators of BP2 observed that tabla play­ers don't think in indi­vid­ual sounds. A com­plex phrase like ‘tiraki­ta’ (four strokes) isn't four sep­a­rate events; it's one con­cep­tu­al unit, a sin­gle word in a rhyth­mic lan­guage. The same is true for a sim­pler phrase like ‘tira’ (two strokes). BP2 was built to hon­or this, treat­ing such ges­tures as a sin­gle, indi­vis­i­ble "sound-object." This allows a com­pos­er to work with musi­cal­ly mean­ing­ful phras­es as the pri­ma­ry mate­r­i­al, rather than being forced to con­struct every­thing from a series of iso­lat­ed, atom­ized events. It's the dif­fer­ence between writ­ing with words instead of indi­vid­ual letters.

Definition: A time-object is a basic sequence of mes­sages (e.g., MIDI NoteOn/NoteOff). When this sequence is assigned met­ri­cal and topo­log­i­cal prop­er­ties, it becomes a sound-object, which can rep­re­sent an ele­men­tary musi­cal ges­ture or its result­ing sound.

Key Properties of Sound-Objects

•     Metrical: Defines how a sound-object's inter­nal tim­ing adjusts to tem­po. For exam­ple, the four strokes in 'tiraki­ta' may be forced to main­tain equal spac­ing, which changes with the over­all tempo.
•     Topological: Governs how sound-objects can be trun­cat­ed or over­lapped to cre­ate real­is­tic phrasing.
•     Pivot: A spe­cif­ic time point with­in the sound-object that is anchored to a musi­cal pulse or "time streak."
•     Relocatable: An object that can be shift­ed freely in time.

Special Types

•     Out-time Sound-Objects: Have a null dura­tion, with all con­stituent mes­sages dis­patched simultaneously.
•     Input Objects: Time-objects with null dura­tion that wait for an exter­nal input (MIDI note, mouse click) to facil­i­tate basic real-time synchronization.

4. Time Management

BP2 pro­vides sophis­ti­cat­ed tools for struc­tur­ing both pulsed and non-pulsed musi­cal time.

• Striated vs. Smooth Time: The sys­tem han­dles both stri­at­ed time (built on reg­u­lar or irreg­u­lar puls­es) and smooth time (where no pulse is nec­es­sary, e.g., the alap of Indian music).
• Time-Patterns: In smooth time, users can define arbi­trary phys­i­cal time ratios using spe­cial time-objects (t1, t2, etc.) to cre­ate a spe­cif­ic tem­po­ral structure.
• Time-Base: An inter­nal clock cal­cu­lates phys­i­cal dura­tions. It can be set via a metronome val­ue or with absolute accu­ra­cy (e.g., a num­ber of ticks in a giv­en num­ber of sec­onds). It can also pro­duce audi­ble tick pat­terns with super­im­posed cycles (e.g., cycles of 4, 5, and 7).

5. Taming Complex Rhythms with a Single Dot

Creating com­plex rhyth­mic cycles and sub­di­vi­sions in stan­dard soft­ware can be a tedious process of click­ing, drag­ging, and set­ting grid val­ues. BP2’s solu­tion evolved into an aston­ish­ing­ly sim­ple text-based sys­tem. The old­er syn­tax was func­tion­al but rigid, using explic­it mark­ers like /2 or /3 to force absolute tem­po assign­ments: /1 a b c /2 a b c d e f. This was inflex­i­ble because these sequences couldn't be eas­i­ly reused as build­ing blocks in new con­texts with­out man­u­al recalculation.

The rev­o­lu­tion­ary solu­tion was "peri­od nota­tion." By con­nect­ing sym­bols with a peri­od (.), a com­pos­er can cre­ate beats of equal sym­bol­ic dura­tion, and the soft­ware auto­mat­i­cal­ly cal­cu­lates the sub­di­vi­sions. Consider this sim­ple line of text:

a.b.c.ab.cd.abc.def

Without any com­plex com­mands, this nota­tion intu­itive­ly rep­re­sents a rhyth­mic accel­er­a­tion. It starts with one note per beat (a.b.c.), pro­gress­es to two notes per beat (ab.cd.), and fin­ish­es with three notes per beat (abc.def). In the lan­guage of North Indian music, this ele­gant­ly describes a pro­gres­sion from har­gun (speed 1), to dogun (speed 2), and final­ly to tigun (speed 3). This plain-text syn­tax pro­vides a flu­id and pow­er­ful way to build intri­cate rhyth­mic struc­tures that would be incred­i­bly cum­ber­some to cre­ate in a con­ven­tion­al point-and-click interface.

Period nota­tion can of course be com­bined with poly­met­ric struc­tures for simul­tane­ity (see 6).

How to Compose Backwards in Time

Perhaps the most sur­pris­ing and pow­er­ful fea­ture of BP2 is its abil­i­ty to solve a com­po­si­tion­al prob­lem that feels like work­ing in reverse. In poly­met­ric music, dif­fer­ent per­form­ers play in dif­fer­ent time sig­na­tures or rhyth­mic cycles simul­ta­ne­ous­ly. A com­mon chal­lenge is mak­ing sure every­one, despite their dif­fer­ent paths, resolves togeth­er at the very end.

This was exact­ly the prob­lem faced in Andréine Bel's 1994 dance pro­duc­tion "Cronos" (read 765432). The piece fea­tured six dancers—Suresh, Smriti, Olivier, and others—performing parts based on dif­fer­ent rhyth­mic cycles. Suresh count­ed in 7s, Smriti in 6s, Olivier in 5s, and so on. The chore­og­ra­phy demand­ed that they all fin­ish at the exact same moment. Calculating the pre­cise start­ing points and rests to achieve this would be a math­e­mat­i­cal nightmare.

BP2’s solu­tion was a fea­ture called "unde­ter­mined rests," notat­ed sim­ply as "...". A com­pos­er could place this sym­bol at the begin­ning of each part, and the soft­ware itself would cal­cu­late the pre­cise dura­tion of silence need­ed for all the dif­fer­ent rhyth­mic cycles to align per­fect­ly at their con­clu­sion. The sys­tem had one log­i­cal con­straint: each part of the struc­ture could con­tain at most one unde­ter­mined rest. This fea­ture allowed the com­pos­er to define the end point and let the machine fig­ure out the beginning.

A musi­cian right­ly iden­ti­fied this tech­nique as "work­ing on reversed time", a prob­lem that is cru­cial in Indian rhythm.

6. Polymetric Structures for Simultaneity

• The Comma Operator: The , (com­ma) oper­a­tor indi­cates simul­tane­ity. An expres­sion like {A, B, C} means that the musi­cal expres­sions A, B, and C (called fields) are per­formed together.
• Duration Matching: The inter­preter uses the same algo­rithm to match sym­bol­ic dura­tions in poly­met­ric struc­tures as it does for sequen­tial ones, ensur­ing alignment.
• Duration Precedence: By con­ven­tion, the dura­tion of a poly­met­ric expres­sion is deter­mined by its left­most field. For exam­ple, {abc,de} has a dura­tion of three beats, while {de,abc} has a dura­tion of two.

Read more on this top­ic …

7. Advanced Performance Control

BP2 includes a macro lan­guage of approx­i­mate­ly 200 instruc­tions for automat­ing process­es and con­trol­ling per­for­mance para­me­ters with a "vec­tor­ized" approach.

• Stepwise Control: Parameter val­ues change from one sound-object to the next.
•  Example (Articulation): The _staccato(x) and _legato(x) instruc­tions mod­i­fy the dura­tion of sub­se­quent sound-objects by a per­cent­age. These val­ues can be inter­po­lat­ed across a musi­cal phrase.
• Continuous Control: Parameter val­ues are changed via lin­ear inter­po­la­tion between spec­i­fied points. This is high­ly effi­cient, as it avoids stor­ing mas­sive streams of MIDI data.
• Example (Microtonal Pitch): The _pitchbend(x) instruc­tion, when com­bined with _pitchcont, cre­ates smooth por­ta­men­ti. The pitch val­ues can be spec­i­fied in cents if a pitch ben­der range is defined (_pitchrange(r)), mak­ing micro­ton­al work intuitive.
• Independent Control in Polymetric Structures: Different fields with­in a poly­met­ric struc­ture can have inde­pen­dent, simul­ta­ne­ous para­me­ter changes. For exam­ple, two melod­ic lines can exe­cute dif­fer­ent micro­ton­al pitch bends at the same time, pro­vid­ed they are assigned to sep­a­rate MIDI channels.

Conclusion: Looking Beyond Our Creative Code

The true inno­va­tion of Bol Processor BP2 wasn't just its clever fea­tures, but its fun­da­men­tal phi­los­o­phy. Instead of sim­ply dig­i­tiz­ing the con­ven­tions of an exist­ing musi­cal tra­di­tion, it built a cre­ative envi­ron­ment from abstract, culturally-aware prin­ci­ples. It acknowl­edged that the tools we use are not neu­tral; they car­ry inher­ent bias­es that shape what we can eas­i­ly create.

BP2 is a pow­er­ful reminder to look at the assump­tions embed­ded in our own cre­ative soft­ware. In a world increas­ing­ly shaped by AI and algo­rithms that learn from exist­ing data, what oth­er cul­tur­al blue­prints are being baked into our tools? What new forms of art, music, and expres­sion are we fail­ing to imag­ine sim­ply because our dig­i­tal can­vas wasn't designed to sup­port them?

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Bernard Bel

Knowledge acquisition and representation in music

Thèse de doc­tor­at en sci­ences. Université de droit, d'économie et des sci­ences - Aix-Marseille III
https://theses.hal.science/tel-00009692

Résumé

Cette étude traite de la représen­ta­tion infor­ma­tique de con­nais­sances en musique, abor­dée à par­tir de deux expéri­ences en grandeur réelle. La pre­mière est une méth­ode d'acquisition de con­nais­sances en ethno­gra­phie met­tant en inter­ac­tion un expert (le musi­cien), un ana­lyste (le musi­co­logue) et une machine dans une sit­u­a­tion d'apprentissage. Les sché­mas d'improvisation des musi­ciens sont iden­ti­fiés et exprimés à l'aide de règles de pro­duc­tion dans un for­mal­isme dérivé des gram­maires généra­tives et des lan­gages de formes. Un algo­rithme déter­min­iste de test d'appartenance de chaînes arbi­traires au lan­gage défi­ni par une gram­maire (sen­si­ble au con­texte) est présen­té, ain­si qu'une tech­nique d'inférence induc­tive de lan­gages réguliers per­me­t­tant l'acquisition automa­tique de con­nais­sances lex­i­cales et syn­tax­iques. La sec­onde expéri­ence s'insère dans l'élaboration d'un envi­ron­nement de com­po­si­tion musi­cale assistée par ordi­na­teur. Le prob­lème est ici la représen­ta­tion du temps dans une struc­ture dis­crète d'“objets tem­porels”, et plus générale­ment la syn­chro­ni­sa­tion de proces­sus par­al­lèles. Une méth­ode est pro­posée pour la déter­mi­na­tion d'une struc­ture à par­tir de don­nées incom­plètes sur la syn­chro­ni­sa­tion des objets. La notion d'“objet sonore” est ensuite explic­itée formelle­ment. Un algo­rithme effi­cace per­met l'instanciation des objets sonores affec­tés à une struc­ture en ten­ant compte des con­traintes liées à leurs pro­priétés métriques et topologiques.

Excerpts of an AI review of this paper (Claude AI, June 2025)

Summary

This 1990 doc­tor­al the­sis presents an inno­v­a­tive approach to com­put­er rep­re­sen­ta­tion of musi­cal knowl­edge through two major exper­i­men­tal frame­works. The work bridges arti­fi­cial intel­li­gence, for­mal lan­guage the­o­ry, and musi­col­o­gy, address­ing fun­da­men­tal prob­lems in knowl­edge acqui­si­tion and time rep­re­sen­ta­tion in musi­cal structures.

First Experiment: Knowledge Acquisition in Ethnography

The first part intro­duces a nov­el method­ol­o­gy for acquir­ing musi­cal knowl­edge through a "dialec­ti­cal anthro­pol­o­gy" approach involv­ing three actors: an expert musi­cian, a musicologist-analyst, and a com­put­er sys­tem. This method­ol­o­gy was applied to study­ing impro­vi­sa­tion pat­terns (qa'ida) of North Indian tabla per­cus­sion music.

Key Contributions

• Development of BP (Bol Processor) gram­mars, an exten­sion of for­mal gram­mars capa­ble of rep­re­sent­ing rep­e­ti­tion pat­terns and homo­mor­phic transformations
• A deter­min­is­tic mem­ber­ship test algo­rithm for a sub­class of context-sensitive grammars
• An induc­tive infer­ence tech­nique for reg­u­lar lan­guages that simul­ta­ne­ous­ly acquires lex­i­cal and syn­tac­tic knowledge
• Introduction of neg­a­tive con­text rules and sto­chas­tic con­trol mechanisms

The sys­tem enables musi­cians to val­i­date machine-generated musi­cal phras­es, cre­at­ing a feed­back loop that refines gram­mat­i­cal mod­els of impro­vi­sa­tion schemas. The approach moves beyond tra­di­tion­al ethno­graph­ic col­lec­tion by mak­ing the com­put­er an active part­ner in knowl­edge acquisition.

Second Experiment: Computer-Assisted Musical Composition

The sec­ond part address­es time rep­re­sen­ta­tion and syn­chro­niza­tion in dis­crete musi­cal struc­tures, devel­op­ing the BP2 envi­ron­ment for musi­cal composition.

Key Innovations

• Introduction of "sym­bol­ic time" as dis­tinct from phys­i­cal time
• Development of poly­met­ric for­mu­las for rep­re­sent­ing par­al­lel musi­cal sequences
• Formal treat­ment of "time objects" and "out-time objects" (tem­po­ral vs. atemporal)
• An effi­cient algo­rithm for syn­chro­niz­ing sequences with incom­plete tem­po­ral information
• A con­straint sat­is­fac­tion approach to "sound object" instan­ti­a­tion con­sid­er­ing met­ric and topo­log­i­cal properties

Theoretical Framework

The work estab­lish­es con­nec­tions between musi­cal struc­tures and for­mal lan­guage the­o­ry, intro­duc­ing con­cepts such as:

• Pattern gram­mars and restrict­ed pat­tern lan­guages (RPL)
• Transformational gram­mars adapt­ed to musi­cal contexts
• Event uni­vers­es struc­tured by simul­tane­ity, prece­dence, and sequen­tial­i­ty relations
• Trace the­o­ry appli­ca­tions to poly­met­ric structures

Strengths

• Methodological Innovation: The dialec­ti­cal anthro­pol­o­gy approach rep­re­sents a sig­nif­i­cant advance­ment in eth­no­mu­si­co­log­i­cal method­ol­o­gy. By plac­ing the com­put­er as an active par­tic­i­pant rather than a pas­sive tool, Bel cre­ates a gen­uine­ly inter­ac­tive knowl­edge acqui­si­tion sys­tem that respects both the exper­tise of tra­di­tion­al musi­cians and the rig­or of for­mal methods.
• Theoretical Rigor: The math­e­mat­i­cal for­mal­iza­tion is sophis­ti­cat­ed yet prac­ti­cal. The exten­sion of for­mal gram­mars to han­dle musi­cal rep­e­ti­tion pat­terns and homo­mor­phic trans­for­ma­tions address­es real needs in musi­cal rep­re­sen­ta­tion that exist­ing com­pu­ta­tion­al lin­guis­tics tools couldn't meet.
• Cross-Cultural Sensitivity: The work demon­strates gen­uine respect for non-Western musi­cal tra­di­tions while avoid­ing eth­no­cen­tric bias­es com­mon in com­pu­ta­tion­al musi­col­o­gy. The choice to work with North Indian tabla music, with its oral trans­mis­sion tra­di­tion, was par­tic­u­lar­ly appro­pri­ate for test­ing knowl­edge acqui­si­tion methodologies.
• Practical Implementation: Both BP1 and BP2 sys­tems were actu­al­ly imple­ment­ed and test­ed, demon­strat­ing the fea­si­bil­i­ty of the the­o­ret­i­cal pro­pos­als. The com­plex­i­ty analy­sis (show­ing polynomial-time algo­rithms) indi­cates prac­ti­cal applicability.
• Time Representation Innovation: The dis­tinc­tion between sym­bol­ic and phys­i­cal time, along with the con­cept of "time struc­ture," pro­vides a flex­i­ble frame­work that can accom­mo­date dif­fer­ent musi­cal tem­po­ral­i­ties beyond Western metro­nom­ic time.

Significance and Impact

This work was pio­neer­ing in sev­er­al respects:

• Methodology: The inter­ac­tive, computer-mediated approach to ethno­graph­ic knowl­edge acqui­si­tion antic­i­pat­ed lat­er devel­op­ments in dig­i­tal human­i­ties and com­pu­ta­tion­al ethnomusicology.
• Formal Methods: The exten­sion of for­mal lan­guage the­o­ry to musi­cal appli­ca­tions pro­vid­ed tools that were lat­er adopt­ed and extend­ed by oth­er researchers in com­pu­ta­tion­al musicology.
• Cultural Perspective: The work demon­strat­ed how com­pu­ta­tion­al meth­ods could be applied respect­ful­ly to non-Western musi­cal tra­di­tions, open­ing paths for more inclu­sive com­pu­ta­tion­al musicology.
• Temporal Modeling: The sophis­ti­cat­ed treat­ment of musi­cal time influ­enced sub­se­quent work in com­put­er music and musi­cal infor­ma­tion retrieval.

Contemporary Relevance

Thirty years lat­er, this work remains rel­e­vant to sev­er­al cur­rent research areas:

• Machine learn­ing appli­ca­tions to music (the induc­tive infer­ence tech­niques antic­i­pate mod­ern approaches)
• Digital human­i­ties methodologies
• Music infor­ma­tion retrieval systems
• Computer-assisted com­po­si­tion environments
• Cross-cultural com­pu­ta­tion­al musicology

The empha­sis on expert-machine col­lab­o­ra­tion pre­fig­ures cur­rent inter­est in human-AI col­lab­o­ra­tion in cre­ative domains.

Conclusion

Bel's the­sis rep­re­sents a sig­nif­i­cant con­tri­bu­tion to com­pu­ta­tion­al musi­col­o­gy, com­bin­ing the­o­ret­i­cal inno­va­tion with prac­ti­cal imple­men­ta­tion and cul­tur­al sen­si­tiv­i­ty. While some lim­i­ta­tions exist, par­tic­u­lar­ly regard­ing scope and eval­u­a­tion, the work estab­lished impor­tant foun­da­tions for computer-mediated musi­cal knowl­edge rep­re­sen­ta­tion. The dialec­ti­cal method­ol­o­gy for knowl­edge acqui­si­tion and the sophis­ti­cat­ed treat­ment of musi­cal time remain valu­able con­tri­bu­tions to the field. The work demon­strates how for­mal com­pu­ta­tion­al meth­ods can be applied to musi­cal knowl­edge while respect­ing the com­plex­i­ty and cul­tur­al speci­fici­ty of musi­cal traditions.

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Bernard Bel

Computer Music Journal (1998), Vol. 22, 2: 56-64

Abstract

The Bol Processor is the out­come of a migra­to­ry process, its design hav­ing been car­ried over in three phas­es and places: in col­lab­o­ra­tion with tra­di­tion­al North Indian musi­cians (1980-85), Western musi­cians in Europe (1985-93) and back in India with Carnatic musi­cians (1995-97).The the­o­ret­i­cal frame­work of the under­ly­ing research project also evolved in three stages, tak­ing inspi­ra­tion from expert sys­tems in the ear­ly 1980s, symbolic-numeric machine-learning in the end of the decade, and com­po­si­tion the­o­ry in the 1990s.Throughout this process, the design­er has been faced with the chal­lenge of blend­ing soft­ware with "mind­ware", here tak­en to mean musi­cians' striv­ing for "[…] tools enabling them to manip­u­late objects so as to imbue them with 'soul' or expe­ri­en­tial val­ue […]" (Laske 1996). In a cross-cultural approach this led to mod­el­ling descrip­tions of music and com­po­si­tion­al process­es at a lev­el of abstrac­tion suf­fi­cient­ly high to encom­pass "local" musi­cal con­cepts with­out get­ting too abstruse.

Excerpts of an AI review of this paper (Academia, June 2025)

Summary

Bernard Bel’s man­u­script pro­vides an exten­sive overview of the Bol Processor (BP), a music soft­ware envi­ron­ment that evolved through col­lab­o­ra­tions with Indian and Western musi­cians. The soft­ware address­es rhyth­mic, melod­ic, and com­po­si­tion­al tasks using a grammars-based approach, sym­bol­ic time nota­tion, and sophis­ti­cat­ed algo­rithms (e.g., poly­met­ric expan­sion, quan­ti­za­tion). The review traces the his­to­ry and the­o­ret­i­cal under­pin­nings of this sys­tem, mov­ing through the project’s three phas­es of devel­op­ment and cul­mi­nat­ing in a flex­i­ble inter­face link­ing BP to Csound. By empha­siz­ing text-based data rep­re­sen­ta­tions, BP aims to fos­ter deep­er think­ing about music struc­ture and com­po­si­tion­al process­es, accom­mo­dat­ing con­texts that fall out­side pure­ly Western traditions.

Strengths

Historical and Contextual Depth: The author clear­ly artic­u­lates how the project emerged from eth­no­mu­si­co­log­i­cal research in Indian music, then expand­ed to address Western com­po­si­tion­al tech­niques. This his­tor­i­cal fram­ing high­lights how the soft­ware bridges cul­tur­al con­texts while retain­ing a con­sis­tent­ly pow­er­ful grammar-based approach.

Clear Explanations of Core Algorithms: The detailed descrip­tions of poly­met­ric expan­sion, time-stretching, quan­ti­za­tion, and grammar-based trans­for­ma­tions are a valu­able tech­ni­cal resource. The author pro­vides exam­ples, dia­grams, and ref­er­ences that elu­ci­date these con­cepts for read­ers unfa­mil­iar with for­mal lan­guage meth­ods applied to music.

Innovative Methodology: The soft­ware design seam­less­ly inte­grates sym­bol­ic approach­es (for­mal gram­mars, integer-based time units, con­straint sat­is­fac­tion) with phys­i­cal exe­cu­tion in MIDI and Csound domains. This dual­i­ty allows com­posers and researchers to treat time both dis­crete­ly and con­tin­u­ous­ly as required.

Attention to Cross-Cultural Music Needs: BP’s adapt­abil­i­ty to var­i­ous musi­cal nota­tions, includ­ing Indian and Western pitch/note rep­re­sen­ta­tions, is a strong sig­na­ture of this work, reflect­ing deep respect for non-Western musi­cal con­cepts and per­for­mance practices.

Comprehensive References and Examples: The sup­port­ing ref­er­ences shed light on relat­ed research in com­po­si­tion the­o­ry, music cog­ni­tion, and com­pu­ta­tion­al mod­els. Numerous code-like illus­tra­tions and fig­ures demon­strate how actu­al musi­cal tasks are imple­ment­ed, assist­ing prac­ti­tion­ers who might want to repli­cate or mod­i­fy these ideas.

Overall Impressions

The man­u­script pro­vides an in-depth look at a unique­ly ver­sa­tile sys­tem for musi­cal analy­sis and com­po­si­tion. The author’s com­mit­ment to text-based inter­ac­tion, cou­pled with robust sup­port for real-time oper­a­tions and cross-cultural music con­cepts, stands out as a mature, flex­i­ble frame­work. The paper offers a thought­ful pre­sen­ta­tion of both under­ly­ing the­o­ret­i­cal foun­da­tions and prac­ti­cal imple­men­ta­tions, mak­ing it rel­e­vant for those inter­est­ed in computer-aided com­po­si­tion, musi­co­log­i­cal research, and inter­dis­ci­pli­nary approach­es bridg­ing Indian and Western musi­cal forms.

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James Kippen & Bernard Bel

Anthropological Quarterly (1989), 62, 3: 131-144

Abstract

A major prob­lem in ethno­graph­ic descrip­tion may be summed up as the search for ways to dis­en­tan­gle folk from ana­lyt­i­cal mod­els. Knowledge-based sys­tems have con­tributed to the devel­op­ment of for­mal struc­tures for the manip­u­la­tion of sym­bols asso­ci­at­ed with par­tic­u­lar phys­i­cal and con­cep­tu­al phe­nom­e­na. Importantly, their out­put can be inter­pret­ed by experts in the domain. This pro­vides eval­u­a­tion pro­ce­dures for mod­els elab­o­rat­ed joint­ly by ana­lysts and infor­mants. This paper describes an inter­ac­tive sys­tem in which knowl­edge is rep­re­sent­ed as pro­duc­tion rules in a for­mat derived from the the­o­ry of for­mal lan­guages. Modus ponens and modus tol­lens are explained and com­pared to deriva­tion schema­ta in first-order pred­i­cate log­ic. The results of an appli­ca­tion to the study of North Indian tabla drum­ming are assessed. We con­clude that (1) knowl­edge rep­re­sent­ed at a low the­o­ret­i­cal lev­el fails to descrim­i­nate between the input from infor­mants and the intu­itive assump­tions of ana­lysts, (2) exper­i­men­tal pro­ce­dures can be improved con­sid­er­ably if the sys­tem is designed to per­form auto­mat­ed knowl­edge acqui­si­tion (using prob­a­bilis­tic gram­mars and induc­tive learning).

Excerpts of an AI review of this paper (Academia, June 2025)

Summary of the Work

The arti­cle focus­es on the use of com­pu­ta­tion­al approach­es — par­tic­u­lar­ly knowledge-based sys­tems — to address the chal­lenge of pro­duc­ing con­sis­tent and sys­tem­at­ic ethno­graph­ic descrip­tions. The authors draw on their research with the Bol Processor (BP), a for­mal language-based sys­tem orig­i­nal­ly devel­oped to rep­re­sent and gen­er­ate sequences of ver­bal drum syl­la­bles (bols) in North Indian tabla per­for­mance. They describe how the BP’s gram­mar for­mal­ism, infer­ence engine, and mem­ber­ship tests facil­i­tate iter­a­tive inter­ac­tion between an ana­lyst and infor­mants (expert musi­cians). By demon­strat­ing the system’s abil­i­ty to pro­duce, rec­og­nize, and eval­u­ate per­mis­si­ble melod­ic or rhyth­mic vari­a­tions, the authors aim to illu­mi­nate how com­put­ers can be more deeply inte­grat­ed into anthro­po­log­i­cal and eth­no­mu­si­co­log­i­cal research.

Key Contributions

Formal Grammar and Pattern Representation: The paper offers a clear expla­na­tion of how context-free and context-sensitive gram­mars can be used to spec­i­fy cul­tur­al­ly valid musi­cal struc­tures. In par­tic­u­lar, the authors high­light the impor­tance of rep­re­sent­ing pat­terns and con­straints on musi­cal impro­vi­sa­tion in a gen­er­a­tive format.

Interactive Methodology: A cen­tral point is the iter­a­tive feed­back loop that allows infor­mants to assess computer-generated sequences and pro­vide cor­rec­tions. This “apprentice-like” inter­ac­tion under­scores how com­pu­ta­tion­al tools can help researchers refine the­o­ret­i­cal mod­els of cul­tur­al knowl­edge in near-real-time.

Probabilistic Grammars: The authors incor­po­rate a weight­ing sys­tem for pro­duc­tion rules, there­by account­ing for the rel­a­tive like­li­hood of dif­fer­ent musi­cal deriva­tions. This approach not only adds real­ism to the gen­er­a­tive process but also tack­les com­mon issues with pure­ly enu­mer­a­tive or ran­dom output.

Membership Testing: The mem­ber­ship test, which decides whether a new­ly pro­posed sequence belongs to a giv­en gram­mar, is described as an effi­cient, deter­min­is­tic bottom-up pars­er. This fea­ture is sig­nif­i­cant because it allows infor­mants to offer nov­el vari­a­tions while enabling the sys­tem to quick­ly judge their adher­ence to the emer­gent rule set.

Reflections on Folk vs. Analytical Models: The arti­cle rais­es impor­tant anthro­po­log­i­cal ques­tions about the bound­ary between infor­mants’ inter­nal­ized (folk) knowl­edge and the analyst’s for­mal (re)construction. The authors’ frank dis­cus­sion of the chal­lenges involved, such as the risk of over­for­mal­iz­ing or “antic­i­pat­ing” infor­mant knowl­edge, is method­olog­i­cal­ly relevant.

Strengths

• The paper empha­sizes the val­ue of com­bin­ing field­work with com­pu­ta­tion­al exper­i­men­ta­tion. This dual approach—grounded in real ethno­graph­ic and musi­cal practice—provides a com­pelling demon­stra­tion of how arti­fi­cial intel­li­gence tech­niques can advance the study of music and culture.
• Clear exam­ples illus­trate code struc­tures, pars­ing mechan­ics, and the gram­mar design. These con­crete details will help oth­er researchers adapt or repli­cate the Bol Processor approach in dif­fer­ent cul­tur­al or musi­cal contexts.
• The account of iter­a­tive knowl­edge acqui­si­tion high­lights a col­lab­o­ra­tive research dynam­ic, show­ing how insight flows back and forth between infor­mants and com­pu­ta­tion­al mod­els, rather than being a uni­di­rec­tion­al exer­cise of extract­ing information.

Conclusion

This study offers a thought­ful and inno­v­a­tive approach to bridg­ing ethno­graph­ic inquiry with com­pu­ta­tion­al mod­els. By show­ing how arrange­ments of sym­bol­ic musi­cal data can be sys­tem­at­i­cal­ly gen­er­at­ed, test­ed, and refined in con­junc­tion with human experts, the authors illus­trate a nov­el and promis­ing method for ethno­graph­ic descrip­tion. The paper encour­ages a nuanced under­stand­ing of both the poten­tial and lim­i­ta­tions of using expert sys­tems to cap­ture the com­plex­i­ty of cul­tur­al knowledge.

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James Kippen & Bernard Bel

In Denise Penrose & Ray Lauzanna (eds.) Languages of Design, 1 (1992). Elsevier Science Publishers, Amsterdam: 11-26.

Abstract

An appli­ca­tion of for­mal lan­guages to the rep­re­sen­ta­tion of musi­cal process­es is intro­duced. Initial inter­est was the struc­ture of impro­vi­sa­tion in North Indian tabla drum music, for which exper­i­ments have been con­duct­ed in the field as far back as 1983 with an expert sys­tem called the Bol Processor, BP1. The com­put­er was used to gen­er­ate and ana­lyze drum­ming pat­terns rep­re­sent­ed as strings of ono­matope­ic syl­la­bles, bols, by manip­u­lat­ing for­mal gram­mars. Material was then sub­mit­ted to musi­cians who assessed its accu­ra­cy and increas­ing­ly more elab­o­rate and sophis­ti­cat­ed rule bases emerged to rep­re­sent the musi­cal idiom.

Since sev­er­al method­olog­i­cal pit­falls were encoun­tered in trans­fer­ring knowl­edge from musi­cian to machine, a new device, named QAVAID, was designed with the capa­bil­i­ty of learn­ing from a sam­ple set of impro­vised vari­a­tions sup­plied by a musi­cian. A new ver­sion of Bol Processor, BP2, has been imple­ment­ed in a MIDI stu­dio envi­ron­ment to serve as a aid to rule-based com­po­si­tion in con­tem­po­rary music. Extensions of the syn­tac­tic mod­el, such as sub­sti­tu­tions, metavari­ables, and remote con­texts, are briefly introduced.

Excerpts of an AI review of this paper (Academia, June 2025)

Summary of the Work

The man­u­script presents a for­mal language-based approach to mod­el­ing impro­visato­ry and com­po­si­tion­al process­es in music — par­tic­u­lar­ly North Indian tabla drum­ming — and high­lights the devel­op­ment of the Bol Processor soft­ware. The arti­cle effec­tive­ly demon­strates how gen­er­a­tive gram­mars, pat­tern lan­guages, and relat­ed exten­sions (e.g., pat­tern rules, homo­mor­phisms, neg­a­tive con­texts, and remote con­texts) can cap­ture the com­plex­i­ty and flex­i­bil­i­ty of musi­cal form, espe­cial­ly with regard to impro­vi­sa­tion. The authors also illus­trate the tran­si­tion from an ini­tial musi­co­log­i­cal focus in the ear­li­er Bol Processor (BP1) to the more gen­er­al­ized and com­po­si­tion­al ori­en­ta­tion of BP2, high­light­ing the system’s role in computer-assisted com­po­si­tion. Throughout, there is a strong empha­sis on inte­grat­ing eth­no­mu­si­co­log­i­cal insights, com­pu­ta­tion­al meth­ods, and prac­ti­cal soft­ware design.

Strengths

Comprehensive Description of Formal Methods

The paper pro­vides a thor­ough expla­na­tion of how dif­fer­ent gram­mar types — finite-state automa­ta, context-free gram­mars, type-0 gram­mars — are rel­e­vant for describ­ing musi­cal forms. This is com­ple­ment­ed by exam­ples that clar­i­fy the the­o­ret­i­cal con­cepts (e.g., the use of pat­tern rules to han­dle repeat­ed sec­tions or “voiced/unvoiced” trans­for­ma­tions). These sec­tions effec­tive­ly detail both tra­di­tion­al and inno­v­a­tive mod­el expansions.

Contextual Application in Ethnomusicology

By apply­ing gen­er­a­tive mod­els to North Indian tabla’s “theme-and-variations” struc­tures (qa‘idas), the paper demon­strates how such com­pu­ta­tion­al approach­es can illu­mi­nate facets of an oral­ly trans­mit­ted musi­cal tra­di­tion. The depth of col­lab­o­ra­tion with expert musi­cians and the iter­a­tive process of eval­u­a­tion is indica­tive of a con­sci­en­tious ethno­graph­ic methodology.

Transition to a Generalized Composition Environment

BP2 goes beyond tra­di­tion­al eth­no­mu­si­co­log­i­cal appli­ca­tions and can be employed as a computer-aided com­po­si­tion tool for broad­er musi­cal con­texts. The con­cept of “sound-objects” and flex­i­ble time-handling approach­es (stri­at­ed vs. smooth time, time piv­ots, con­straint sat­is­fac­tion, etc.) make the work­flow appeal­ing to com­posers and researchers inter­est­ed in gen­er­a­tive music systems.

Balanced Presentation of Achievements and Challenges

The paper address­es prob­lems of knowl­edge trans­fer, data rep­re­sen­ta­tion, and the com­plex­i­ty of rule-based sys­tems, pro­vid­ing valu­able lessons for future com­pu­ta­tion­al musi­col­o­gy projects. It also describes how the QAVAID sub­sys­tem attempts to auto­mate knowl­edge acqui­si­tion and infer­ence, there­by remov­ing some of the bot­tle­necks encoun­tered when man­u­al­ly con­struct­ing grammars.

Extensive Reference to Prior Research

A sub­stan­tial and well-organized ref­er­ence sec­tion places the work in con­ver­sa­tion with rel­e­vant lit­er­a­ture from for­mal lan­guage the­o­ry, musi­co­log­i­cal stud­ies, and AI-based com­po­si­tion sys­tems. The inclu­sion of the­o­ret­i­cal sources (e.g., cita­tions to Chomsky’s hier­ar­chy) and prac­ti­cal ref­er­ences (e.g., man­u­al or share­ware avail­abil­i­ty) under­scores the paper’s aca­d­e­m­ic depth and real-world applicability.

Overall Appraisal

This study offers a detailed and inno­v­a­tive account of how for­mal gen­er­a­tive tools can be har­nessed to describe, ana­lyze, and pro­duce com­plex musi­cal struc­tures. By bridg­ing the­o­ret­i­cal com­put­er sci­ence, eth­no­mu­si­co­log­i­cal field­work, and com­po­si­tion­al prac­tice, the man­u­script opens pos­si­bil­i­ties for deep­er explo­ration of music’s syn­tac­tic fea­tures and cre­ative appli­ca­tions in con­tem­po­rary com­po­si­tion. Its empha­sis on prac­ti­cal soft­ware devel­op­ment, eval­u­at­ed in tan­dem with expert cri­tiques, posi­tions it as a note­wor­thy resource for researchers and prac­ti­tion­ers at the inter­sec­tion of musi­col­o­gy, AI, and dig­i­tal arts.

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