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This work presents the model of a system that generates guitar rhythmic accompaniment of a song, given its chord grid and melody. In order to minimize the effects of the lack of formal knowledge which inherently exists in this musical dimension, the system uses a very natural approach in music: the contextualized reuse of rhythmic patterns, found i...
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... creating accompaniment and solo to electric guitar, piano, bass, drums and strings in a large number of styles (although the user may create his/her own ones). It has a number of musical fragments (up to 1620 per style) of different length (1 to 4 measures) using single chord granularity. In Band-in-a-box, there is no attributes concerning musical property of the fragments, just environment ones. Concerning the retrieval criteria, the software uses a random choice biased by user-entry weights to each fragment; using a simple set of rules to restrict the group of fragments that disagree with the environment attributes. For instance, there are some fragments that can only be played in the beginning of the song; others are supposed to be played in the end, and so on. Just transposition adaptation is provided. Although the Band-in-a-Box approach is very attractive because of its simplicity and flexibility, we were most interested in musical plausibility, and the ImPact system was more qualified regarding this aspect. In order to evaluate in what extent ImPact approach could be adapted to the automatic generation of guitar rhythmic accompaniment, we chose a specific musical style: Bossa Nova. The advantages of choosing such a well-known and documented style is to benefit from the knowledge concerning rhythmic patterns description [4], as well as to better evaluate the system performance. In this context, we decided to reuse rhythmic fragments played by João Gilberto, since they are the most representative ones in Bossa Nova [9]. Due to the change of instrument (from bass to guitar), style (from Jazz to Bossa Nova) and environment (from ensemble to voice-and-guitar), the main implemented modifications of the original ImPact were: new library of musical fragments, new attributes of these fragments, new rules to improve the fragments retrieval and new perceptions (acquired from melody). It is important to emphasize that some of these new elements are difficult to elicit (as compared to the case of Jazz bass lines) due to the poor formalization of rhythmic accompaniment for guitar. The task of choosing Bossa Nova rhythmic fragments was simplified by two musicology works [4] [9]. They transcribed and analyzed João Gilberto’s most important rhythmic patterns and highlighted some important features: All patterns last two measures (in 2/4 signature); Just two kinds of events are allowed (one produced by the thumb and the other, produced by forefinger, middle finger, and ring finger together, which we call, from now on, “plucking block”); Several patterns anticipate their first event producing syncopation, an important rhythmic characteristic of Bossa Nova style. There are four groups of patterns: the cyclic, beginning, special and fill-in, as depicted in Figure 1 (thumb in the lower clef and plucking block attacks in upper one); There is a clear difference in performance of slow Bossa Nova songs (less than 120 quarter notes per minute) and faster ones, some patterns being forbidden to be used in the former. Based on the above considerations, we decided that our rhythmic patterns would have fixed length and two- measure granularity. For the same reason, we did not implemented adaptations on the Bossa Nova patterns: they will be reused exactly as played by João Gilberto. Each pattern is represented by a small but effective set of attributes. They are of two kinds: environmental and musical ones. Environmental attributes describe the context where the pattern has been used. They are: Harmonic rhythm , indicating how harmony (chords) changes in a given period of time (e.g. one or two measures). This attribute can have 15 different values ranging from a single chord lasting two 2/4 measures to four chords (one per beat); Tempo , which can assume 2 values: “slow” (< 120 quarters per minute) or “fast”. Musical attributes describe the pattern's musical properties, i.e., its characteristics. They are: Beginning , a binary attribute pointing out whether a pattern started at the down beat; Fill-in , binary attribute indicating whether the pattern has been used as a “fill-in accompaniment”, i.e., fragments commonly used when there is no melody been sung, or in turnarounds and turn backs. Usage , determining how frequently the pattern has been used. The values range from 5 (maximum usage) and 1 (minimum usage); Density , describing the number of musical events in each pattern’s measure. The possible values are “High”, “Medium”, and “Low”. Table 1 shows an example of rhythmic pattern and its description. We have identified and indexed, with the help of experts and the literature [4] [9], 21 patterns in the library. Following the ImPact approach, the retrieval technique consists in a mixed use of Rule-Based and Case-Based Reasoning. The former is used to extract from the environment (in our case, the chord grid and the melody) characteristics that suggest a musical intention (patterns’ musical properties), as exemplified in Figure 2. To achieve these rules, we used the traditional method of knowledge acquisition [8] in which we interviewed specialists obtaining, after analysis, 6 rules that were consensus for all of them. Case-Based Reasoning is employed to effectively choose the best pattern in the library (case base). In order to improve the retrieval, all attributes are weighted from 5 to 1, according to their importance, again obtained through specialists, as normally used in Case-Based systems. The query is performed by k- nearest neighbors [6]. In order to analyze the real effectiveness of our approach, we have implemented Cyber-João. Also, to improve this analysis, we have implemented two other systems (less complex than the former): Crazy-João and João-in-a-Box. The first one selects the patterns to be used in the song by random choice; it is the baseline of our experiment. The second one, use the same retrieve criteria found in Band-in-a-Box, which is random choice bounded by weights and constrained by rules. The great benefit about these two new approaches is that they are not related with the style and demand little programming effort, instead of the approach used in ImPact/Cyber-João. So, we evaluated here if a knowledge intensive approach is really better in this case. Thanks to modularity, it was possible to reuse a great amount of Cyber-João’s code in implementation of Crazy-João and João-in-a-Box. However, it is important to note that there is a subtle difference on the effect of the set of rules between Cyber-João and João-in-a-Box. Whereas the former acts on the musical properties of the pattern being retrieved, the second acts directly in the rule base, restricting the groups to be used in a given moment (like Band-in-a-box). We used four classical Bossa Nova songs: Chega de Saudade, Desafinado, Insesatez and Lígia . The election of the songs obeyed two criteria: Availability of a good- quality melody in MIDI format and the tempo of the songs (the two first ones are considered fast, while the other two, slow ones). Each song was generated by each three systems, resulting in a corpus of twelve samples. The corpus was blinded and presented to six evaluators, all of them, Bossa Nova musicians. They answered to a questionnaire in which they had to point out a score (among bad, acceptable, good, and excellent) for each sample and then, the best version for each song (ties were allowed). Table 2, shows the score summarization grouped by system. Based on this data, we can perceive a clear difference between the Crazy-João results and the other’s. While less than 9% of the evaluations were at least good for this system, the others received at least more than 66%. Obviously, as one could imagine, the use of random choice to select the rhythm patterns in the Bossa Nova style is completely discouraged. The Table 3 summarizes the results of the best version for each song. There were three ties: two in Desafinado and one in Insensatez, so the sum of the total goes to 27 rather than 24. The results show that Cyber-João was evaluated as the best version more than 62% of the times; almost two times Joao-in-a-box did. In this paper, we presented a model for generating the rhythmic accompaniment for guitar, based on the reuse of previously stored rhythmic fragments. To validate this model we implemented Cyber-João, a system that plays Bossa Nova. It was coded in C++ and is fully integrated to D’Accord Guitar. It takes as input a dv3 file (D’Accord Guitar format) containing the melody and chord grid of a given song. Reusing a rhythmic pattern library, Cyber-João generates the correspondent accompaniment. It is important to note however, that this is a general approach to the rhythmic generation problem. According to musicians’ analysis of the preliminary outcome, Cyber-João exhibits excellent results: it was evaluated as good and excellent in 75% of the cases. We are now coding a large corpus of Bossa Nova songs in order to provide a more precise evaluation of Cyber-João. In this perspective we intend to develop a feedback support system to help experts to judge and comment agent’s choice and automatically generating statistical results, which can be used to improve the agent’s knowledge. The preliminary results encourage us to reuse the same approach in other musical styles. This is reinforced by the fact that the rhythmic fragment attributes in Cyber-João library are quite generic. Besides, the combination of this approach with an automatic extractor of rhythmic pattern [10] would speed up the development of new guitar accompaniment ...
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... To avoid the repetition problem, there has been an effort towards developing systems capable of learning rhythms from a corpus and predicting new patterns . Techniques such as rule-based probabilistic re- combination [12], artificial life [13] and genetic algo- rithms [14] were used in this context. This gives rise to another form of human-computer interaction, in which the machine yields unpredictable outcomes. ...
... This behaviour is close, but not exactly the same as, the one yielded by the method proposed by Assayag et al. [11] , as it is deterministic and learned from data yielded by a human being in real-time. Hence, the system does not aim at generating new sequences as the previous approaches discussed above121314. However, it is possible to change the outcomes algorithm by increasing the onset tolerance α to values that are close to a typical inter-onset interval. ...
In electronic music, it is often useful to build loops from discrete events, such as playing notes or triggering digital e�ects. This process generally requires using a visual interface, as well as pre-de�ning tempo and time quantization. We present a novel digital musical instrument capable of looping events without using visual interfaces or explicit knowledge about tempo or time quantization. The instrument is built based on a prediction algorithm that detects repetitive patterns over time, allowing the construction of rhythmic layers in real-time performances. It has been used in musical performances, where it showed to be adequate in contexts that allow improvisation.
... While the aforementioned systems perform in real-time, an off-line score-constrained system has also been proposed for finding jazz-style arrangement of a melody and its chord sequence [5]. A real-time system that provides a jazz style bass line given a chord grid and the improvisation content of a musician has been presented in [6] and a system that performs the same task for guitar accompaniment has been presented in [7]. ...
Computational Intelligence encompasses tools that allow the fast convergence and adaptation to several problems, a fact that makes them eligible for real-time implementations. The paper at hand discusses the utilization of intelligent algorithms (i.e. Differential Evolution and Genetic Algorithms) for the creation of an adaptive system that is able to provide real-time automatic music accompaniment to a human improviser. The main goal of the presented system is to generate accompanying music based on the local human musician's tonal, rhythmic and intensity playing style, incorporating no prior knowledge about the improvisers intentions. Compared to existing systems previously proposed, this work introduces a constraint-free improvisation environment where the most important musical characteristics are automatically adapted to the human performer's playing style, without any prior information. This fact allows the improviser to have maximal control over the tonal, rhythmic and intensity improvisation directions.
... Computationally, this problem is difficult if g(t) is sought independently of f (t) because the space of possible such functions is infinite and unstructured. Thus one approach is to enumerate a comprehensive set of rules that in effect describe how g(t) is constructed [15,16]. However, the insight in this paper is simpler and interesting because it requires no musical analysis: The function g(t) can be constrained to a promising set simply by searching instead for h(f (t)), where h outputs the accompaniment instead of g(t). ...
A popular approach to music generation in recent years is to extract rules and statistical relationships by analyzing a large corpus of musical data. The aim of this paper is to present an alternative to such data-intensive techniques. The main idea, called functional scaffolding for musical composition (FSMC), exploits a simple yet powerful prop-erty of multipart compositions: The pattern of notes and rhythms in different instrumental parts of the same song are functionally related. That is, in principle, one part can be expressed as a function of another. The utility of this insight is validated by an application that assists the user in exploring the space of possible accompaniments to pre-existing parts through a process called interactive evolu-tionary computation. In effect, without the need for musi-cal expertise, the user explores transforming functions that yield plausible accompaniments derived from preexisting parts. In fact, a survey of listeners shows that participants cannot distinguish songs with computer-generated parts from those that are entirely human composed. Thus this one simple mathematical relationship yields surprisingly convincing results even without any real musical knowl-edge programmed into the system. With future refinement, FSMC might lead to practical aids for novices aiming to fulfill incomplete visions.
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CinBalada is a system for automatic creation of polyphonic rhythmic performances by mixing elements from different musical styles. This system is based on agents that act as musicians playing percussion instruments in a drum circle. Each agent has to choose from a database the rhythm pattern of its instrument that satisfies the “rhythmic role” assigned to him in order to produce a collectivelyconsistent rhythmic performance. A rhythmic role is a concept that we proposed here with the objective of representing culture-specific rules for creation of polyphonic performances.
... We will call them " percussive composition systems " in the rest of this paper. This classification does not include systems that compose rhythmic sequences not bound to any particular percussion instrument but intended to aid musicians in the composition of musical parts for other instruments, as [21] and [5]. A great number of techniques can be employed in automatic composition. ...
Abstrac
CinBalada is a system for automatic creation of polyphonic rhythmic performances by mixing elements from different musical styles. This system is based on agents that act as musicians playing percussion instruments in a drum circle. Each agent has to choose from a database the rhythm pattern of its instrument that satisfies the “rhythmic role” assigned to him in order to produce a collectivelyconsistent rhythmic performance. A rhythmic role is a concept that we proposed here with the objective of representing culture-specific rules for creation of polyphonic performances.
... First of all, let us examine the patterns used by the performers. As described by Dahia et al. [2], patterns in the catalogue belong to one of four groups: cyclical (P 2 to P 8), beginning (P 9 to P 12), special (P 13 and P 14) and fill-in patterns (P 15 to P 21). Pattern P 1 is the main Bossa Nova pattern and forms its own group. ...
The analysis of expressive performance, an important re- search topic in Computer Music, is almost exclusively de- voted to the study of Western Classical piano music. Instru- ments like the acoustic guitar and styles like Bossa Nova and Samba have been little studied, despite their harmonic and rhythmic richness. This paper describes some experimen- tal results obtained with the extraction of rhythmic patterns from the guitar accompaniment of Bossa Nova songs. The songs, played by two different performers and recorded with the help of a MIDI guitar, were represented as strings and processed by FlExPat, a string matching algorithm. The re- sults obtained were then compared to a previously acquired catalogue of "good" patterns.
... For this, two different performers played several songs on a MIDI guitar, which were processed in the form of strings. Based on a catalogue of Bossa Nova patterns [3]), we tried to identify how much of it could be automatically found in the data we collected 6 . We also discuss the cultural issues behind the experiment. ...
... Dahia et al. [3] used a catalogue containing 21 rhythmic patterns of Bossa Nova guitar music, acquired manually from João Gilberto's 7 We assume the player is right handed. 8 The terms "baixo" and "puxada" may explain more clearly the origin of letters b and p! performances, to automatically generate the guitar accompaniment of some songs. ...
The analysis of expressive performance, an important re-search topic in Computer Music, is almost exclusively devoted to the study of western classical piano music. Instruments like the acous-tic guitar and styles like Bossa Nova and Samba have almost never been studied, despite their harmonic and rhythmic richness. This pa-per describes some experimental results obtained with the extraction of rhythmic patterns from the guitar accompaniment of Bossa Nova songs. It also discuss the relevance of such experiments, as well as the importance of the computer techniques used in them, to the music and musical culture in general.
... For this, two different performers played several songs on a MIDI guitar, which were processed in the form of strings. Based on previous work (the acquisition of a catalogue of Bossa Nova patterns [Dahia et al., 2004]), we tried to identify how much of this catalogue could be automatically found in the data we collected 1 . ...
The analysis of musical interpretation or expressive performance, an important research topic in Computer Music, is almost exclusively devoted to the study of western classical music for the piano. Instruments like the acoustic guitar and styles like Bossa Nova and Samba have almost never been studied, despite their harmonic and rhythmic richness. This paper describes some exper-imental results obtained with the extraction of rhythmic patterns from the gui-tar accompaniment of Bossa Nova songs. The performances were represented as strings and processed by three different string matchers. Results obtained showed that parts of a previously acquired catalogue of patterns could be found in the data set. Surprising results, such as very long patterns, also showed up.
