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

An Introduction to MultiAgent Systems.

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Digital processing of speech signals

: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

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Nonlinear Time Series Analysis.

We present an exhaustive review of research on automatic classification of sounds from musical instruments. Two different but complementary approaches are examined, the perceptual approach and the taxonomic approach. The former is targeted to derive perceptual similarity functions in order to use them for timbre clustering and for searching and retrieving sounds by timbral similarity. The latter is targeted to derive indexes for labeling sounds after culture- or user-biased taxonomies. We review the relevant features that have been used in the two areas and then we present and discuss different techniques for similarity-based clustering of sounds and for classification into pre-defined instrumental categories.

/pdf/automatic-classification-of-musical-instrument-sounds-3vn5i2ho3f.pdf

Automatic Classification of Musical Instrument Sounds

This paper presents ongoing work on gesture mapping strategies and applications to sound synthesis
by signal models controlled via a standard MIDI wind controller. Our approach consists in 
considering different mapping strategies in order to achieve "fine" (therefore in the authors' 
opinion, potentially expressive) control of additive synthesis by coupling originally independent 
outputs from the wind controller. These control dignals are applied to nine different clarinet data 
files obtained from analysis of clarinet sounds, which are arranged in an expressive timbral subspace 
and interpolated in real-time, using FTS 1.4, IRCAM's digital signal processing environment. An analysis 
of the resulting interpolation is also provided and topics related to sound morphing are discussed.

Instrumental Gestural Mapping Strategies as Expressivity Determinants in Computer Music Performance

We describe variable markov models we have used for statistical learning of musical sequences, then we present the factor oracle, a data structure proposed by Crochemore & al for string matching. We show the relation between this structure and the previous models and indicate how it can be adapted for learning musical sequences and generating improvisations in a real-time context.

/pdf/using-factor-oracles-for-machine-improvisation-1e7il48z8z.pdf

Using Factor Oracles for Machine Improvisation

The ability to construct a musical theory from examples presents a great intellectual challenge that, if successfully met, could foster a range of new creative applications. Inspired by this challenge, we sought to apply machine-learning methods to the problem of musical style modeling. Our work so far has produced examples of musical generation and applications to a computer-aided composition system. Machine learning consists of deriving a mathematical model, such as a set of stochastic rules, from a set of musical examples. The act of musical composition involves a highly structured mental process. Although it is complex and difficult to formalize, it is clearly far from being a random activity. Our research seeks to capture some of the regularity apparent in the composition process by using statistical and information theoretic tools to analyze musical pieces. The resulting models can be used for inference and prediction and, to a certain extent, to generate new works that imitate the style of the great masters.

Using machine-learning methods for musical style modeling

We describe a multi-agent architecture for an improvization oriented musician-machine interaction system that learns in real time from human performers. The improvization kernel is based on sequence modeling and statistical learning. The working system involves a hybrid architecture using two popular composition/perfomance environments, Max and OpenMusic, that are put to work and communicate together, each one handling the process at a different time/memory scale. The system is capable of processing real-time audio/video as well as MIDI. After discussing the general cognitive background of improvization practices, the statistical modeling tools and the concurrent agent architecture are presented. Finally, a prospective Reinforcement Learning scheme for enhancing the system's realism is described.

Shlomo Dubnov

Papers

Automatic Classification of Musical Instrument Sounds

Instrumental Gestural Mapping Strategies as Expressivity Determinants in Computer Music Performance

Using Factor Oracles for Machine Improvisation

Using machine-learning methods for musical style modeling

OMax brothers: a dynamic yopology of agents for improvization learning