Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Principle Component Analysis

This book addresses the central problem of music cognition: how listeners' responses move beyond mere registration of auditory events to include the organization, interpretation, and remembrance of these events in terms of their function in a musical context of pitch and rhythm. Equally important, the work offers an analysis of the relationship between the psychological organization of music and its internal structure. Combining over a decade of original research on music cognition with an overview of the available literature, the work will be of interest to cognitive and physiological psychologists, psychobiologists, musicians, music researchers, and music educators.

Cognitive Foundations Of Musical Pitch

This book, based on ethnographic fieldwork conducted in Mumbai, provides the uninitiated reader with a thorough introduction to the world of Bollywood cinema. The book provides an in-depth descript...

Bollywood: a guidebook to popular Hindi cinema

Comunicacio presentada a la 53rd International Conference: Semantic audio, celebrada els dies 27 a 29 de gener de 2014 a Londres, Regne Unit.

Creating Research Corpora for the Computational Study of Music: the case of the CompMusic Project

Ragas are characterized by their melodic motifs or catch phrases that constitute strong cues to the raga identity for both the performer and the listener, and therefore are of great interest in music retrieval and automatic transcription. While the characteristic phrases, or pakads, appear in written notation as a sequence of notes, musicological rules for interpretation of the phrase in performance in a manner that allows considerable creative expression, while not transgressing raga grammar, are not explicitly defined. In this work, machine learning methods are used on labelled databases of Hindustani and Carnatic vocal audio concerts to obtain phrase classification on manually segmented audio. Dynamic time warping and HMM based classification are applied on time series of detected pitch values used for the melodic representation of a phrase. Retrieval experiments on raga-characteristic phrases show promising results while providing interesting insights on the nature of variation in the surface ...

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Classification of Melodic Motifs in Raga Music with Time-series Matching

Comunicacio presentada a la 17th International Society for Music Information Retrieval Conference (ISMIR 2016), celebrada els dies 7 a 11 d'agost de 2016 a Nova York, EUA.

Time-Delayed Melody Surfaces for Rāga Recognition.

A prominent categorization of Indian classical music is the Hindustani and Carnatic traditions, the two styles having evolved under distinctly different historical and cultural influences. Both styles are grounded in the m elodic and rhythmic framework of raga and tala. The styles differ along dimensions such as instrumentation, aesthetics and voice production. In particular, Carnatic music is perceived as being more ornamented. The hypothesis that style distinctions are embedded in the m elodic contour is validated via subjective classification tests. Melodic features representing the distinctive cha racteristics are extracted from the audio. Previous work based on the extent of stable pitch regions is supported by measurements of musicians’ annotations of stable notes. Further, a new feature is introduced that captures the presence of specific pitch modulations characteristic of ornamentation in Indian classical music. The combined features show high classification accuracy on a database of vocal music of prominent artist es. The misclassifications are seen to match actual listener confusions.

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Classification of indian classical vocal styles from melodic contours

Data-Driven Exploration of Melodic Structure in Hindustani Music.

This paper provides insights into aspects of tempo and rhythmic elaboration in Hindustani music, based on a study of a large corpus of recorded performances. Typical tempo developments and stress patterns within a metrical cycle are computed, which we refer to as tempo and rhythm patterns, respectively. Rhythm patterns are obtained by aggregating spectral features over metrical cycles. They reflect percussion patterns that are frequent in the corpus, and enable a discussion of the relation between such patterns and the underlying metrical framework, the taal. Tempo patterns, on the other hand, are computed using reference beat annotations. They document the dynamic development of tempo throughout a metrical cycle, and reveal insights into the flexibility of time in Hindustani music for the first time using quantitative methods on a large set of performances. Focusing on aspects of tempo and rhythm, we demonstrate the value of a computational methodology for the analysis of large music corpora by revealing the range of tempi used in performances, intra-cycle tempo dynamics and percussion accents at different positions of the taal cycle.

/pdf/aspects-of-tempo-and-rhythmic-elaboration-in-hindustani-3loqtf2fpc.pdf

Kaustuv Kanti Ganguli

Papers

Classification of Melodic Motifs in Raga Music with Time-series Matching

Time-Delayed Melody Surfaces for Rāga Recognition.

Classification of indian classical vocal styles from melodic contours

Data-Driven Exploration of Melodic Structure in Hindustani Music.

Aspects of Tempo and Rhythmic Elaboration in Hindustani Music: A Corpus Study