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This paper surveys long-term research on the problem of rendering expressive music by means of AI techniques with an emphasis on Case-Based Reasoning. Following a brief overview discussing why people prefer listening to expressive music instead of non-expressive synthesized music, we examine a representative selection of well-known approaches to expressive computer music performance with an emphasis on AI-related approaches. In the main part of the paper we focus on the existing CBR approaches to the problem of synthesizing expressive music, and particularly on TempoExpress, a case-based reasoning system developed at our Institute, for applying musically acceptable tempo transformations to monophonic audio recordings of musical performances. Finally we briefly describe an ongoing extension of our previous work consisting of complementing audio information with information about the gestures of the musician. Music is played through our bodies, therefore capturing the gesture of the performer is a fundamental aspect that has to be taken into account in future expressive music renderings. This paper is based on the “2011 Robert S. Engelmore Memorial Lecture” given by the first author at AAAI/IAAI 2011.

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Thanks to advances in computing power, we can analyze music in radically new and different ways. Computers are still far from grasping some of the deep and often unexpected nuances that release our most intimate emotions. However, by processing vast amounts of raw data and performing unprecedented large-scale analyses beyond the reach of teams of human experts, they can provide valuable insight into some of the most basic aspects of musical discourse, including the evolution of popular music over the years. Has there been an evolution? Can we measure it? And if so, what do we observe?

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09GrayFINAL.pdf

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In this study we compare the use of different music representations for retrieving alternative performances of the same musical piece, a task commonly referred to as version identification. Given the audio signal of a song, we compute descriptors representing its melody, bass line and harmonic progression using state-of-the-art algorithms. These descriptors are then employed to retrieve different versions of the same musical piece using a dynamic programming algorithm based on nonlinear time series analysis. First, we evaluate the accuracy obtained using individual descriptors, and then we examine whether performance can be improved by combining these music representations (i.e. descriptor fusion). Our results show that whilst harmony is the most reliable music representation for version identification, the melody and bass line representations also carry useful information for this task. Furthermore, we show that by combining these tonal representations we can increase version detection accuracy. Finally, we demonstrate how the proposed version identification method can be adapted for the task of query-by-humming. We propose a melody-based retrieval approach, and demonstrate how melody representations extracted from recordings of a cappella singing can be successfully used to retrieve the original song from a collection of polyphonic audio. The current limitations of the proposed approach are discussed in the context of version identification and query-by-humming, and possible solutions and future research directions are proposed.

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salamon-mmir12.pdf

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By mining user-generated text content we can obtain music-related information that could not otherwise be extracted from audio signals or symbolic score representations. In this paper we propose a methodology for extracting musically-relevant semantic information from an online discussion forum, rasikas.org, dedicated to the Carnatic music tradition. For that we define a dictionary of relevant terms such as raagas, taalas, performers, composers, and instruments, and create a complex network representation by matching such dictionary against the forum posts. This network representation is used to identify popular terms within the forum, as well as relevant co-occurrences and semantic relationships. This way, for instance, we are able to guess the instrument of a performer with 95% accuracy, to discover the confusion between two raagas with different naming conventions, or to infer semantic relationships regarding lineage or musical influence. This contribution is a first step towards the creation of ontologies for a culture-specific art music tradition.

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355_ISMIR_2012.pdf

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Intonation is an important concept in Carnatic music that is characteristic of a raaga, and intrinsic to the musical expression of a performer. In this paper we approach the description of intonation from a computational perspective, obtaining a compact representation of the pitch track of a recording. First, we extract pitch contours from automatically selected voice segments. Then, we obtain a a pitch histogram of its full pitch-range, normalized by the tonic frequency, from which each prominent peak is automatically labelled and parametrized. We validate such parametrization by considering an explorative classification task: three raagas are disambiguated using the characterization of a single peak (a task that would seriously challenge a more naïve parametrization). Results show consistent improvements for this particular task. Furthermore, we perform a qualitative assessment on a larger collection of raagas, showing the discriminative power of the entire representation. The proposed generic parametrization of the intonation histogram should be useful for musically relevant tasks such as performer and instrument characterization.

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199_ISMIR_2012.pdf

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Content-based approaches to music retrieval are of great relevance as they do not require any kind of manually generated annotations. In this paper, we introduce the concept of structure fingerprints, which are compact descriptors of the musical structure of an audio recording. Given a recorded music performance, structure fingerprints facilitate the retrieval of other performances sharing the same underlying structure. Avoiding any explicit determination of musical structure, our fingerprints can be thought of a probability density function derived from a self-similarity matrix. We show that the proposed fingerprints can be compared using simple Euclidean distances without using any kind of complex warping operations required in previous approaches. Experiments on a collection of Chopin Mazurkas reveal that structure fingerprints facilitate robust and efficient content-based music retrieval. Furthermore, we give a musically informed discussion that also deepens the understanding of the popular Mazurka dataset.

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055_ISMIR_2012.pdf

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Popular music is a key cultural expression that has captured listeners' attention for ages. Many of the structural regularities underlying musical discourse are yet to be discovered and, accordingly, their historical evolution remains formally unknown. Here we unveil a number of patterns and metrics characterizing the generic usage of primary musical facets such as pitch, timbre, and loudness in contemporary western popular music. Many of these patterns and metrics have been consistently stable for a period of more than fifty years. However, we prove important changes or trends related to the restriction of pitch transitions, the homogenization of the timbral palette, and the growing loudness levels. This suggests that our perception of the new would be rooted on these changing characteristics. Hence, an old tune could perfectly sound novel and fashionable, provided that it consisted of common harmonic progressions, changed the instrumentation, and increased the average loudness.

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srep00521.pdf

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Popular music is a key cultural expression that has captured listeners' attention for ages. Many of the structural regularities underlying musical discourse are yet to be discovered and, accordingly, their historical evolution remain formally unknown. In this contribution we use tools and concepts from statistical physics and complex networks to study and quantify the evolution of western contemporary popular music. In it, we unveil a number of patterns and metrics characterizing the generic usage of primary musical facets such as pitch, timbre, and loudness. Moreover, we find many of these patterns and metrics to be consistently stable for a period of more than fifty years, thus pointing towards a great degree of conventionalism in this type of music. Nonetheless, we prove important changes or trends. These are related to the restriction of pitch transitions, the homogenization of the timbral palette, and the growing loudness levels. The obtained results suggest that our perception of new popular music would be largely rooted on these changing characteristics. Hence, an old tune could perfectly sound novel and fashionable, provided that it consisted of common harmonic progressions, changed the instrumentation, and increased the average loudness.

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Popular music is a key cultural expression that has captured listeners' attention for ages. Many of the structural regularities underlying musical discourse are yet to be discovered and, accordingly, their historical evolution remain formally unknown. In this contribution we use tools and concepts from statistical physics and complex networks to study and quantify the evolution of western contemporary popular music. In it, we unveil a number of patterns and metrics characterizing the generic usage of primary musical facets such as pitch, timbre, and loudness. Moreover, we find many of these patterns and metrics to be consistently stable for a period of more than fifty years, thus pointing towards a great degree of conventionalism in this type of music. Nonetheless, we prove important changes or trends. These are related to the restriction of pitch transitions, the homogenization of the timbral palette, and the growing loudness levels. The obtained results suggest that our perception of new popular music would be largely rooted on these changing characteristics. Hence, an old tune could perfectly sound novel and fashionable, provided that it consisted of common harmonic progressions, changed the instrumentation, and increased the average loudness.

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Locating boundaries between coherent and/or repetitive segments of a time series is a challenging problem pervading many scientific domains. In this paper we propose an unsupervised method for boundary detection, combining three basic principles: novelty, homogeneity, and repetition. In particular, the method uses what we call structure features, a representation encapsulating both local and global properties of a time series. We demonstrate the usefulness of our approach in detecting music structure boundaries, a task that has received much attention in recent years and for which exist several benchmark datasets and publicly available annotations. We find our method to significantly outperform the best accuracies published so far. Importantly, our boundary approach is generic, thus being applicable to a wide range of time series beyond the music and audio domains.

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AAAI12-247c.pdf