Data Mining Practical Machine Learning Tools and Techniques

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

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An Introduction To The Event Related Potential Technique

The mismatch negativity (MMN) in basic research of central auditory processing: a review.

In the present article, the basic research using the mismatch negativity (MMN) and analogous results obtained by using the magnetoencephalography (MEG) and other brain-imaging technologies is reviewed. This response is elicited by any discriminable change in auditory stimulation but recent studies extended the notion of the MMN even to higher-order cognitive processes such as those involving grammar and semantic meaning. Moreover, MMN data also show the presence of automatic intelligent processes such as stimulus anticipation at the level of auditory cortex. In addition, the MMN enables one to establish the brain processes underlying the initiation of attention switch to, conscious perception of, sound change in an unattended stimulus stream. 2007 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

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Invited review The mismatch negativity (MMN) in basic research of central auditory processing: A review

We present the MIRtoolbox, an integrated set of functions written in Matlab, dedicated to the extraction of musical features from audio files The design is based on a modular framework: the different algorithms are decomposed into stages, formalized using a minimal set of elementary mechanisms, and integrating different variants proposed by alternative approaches – including new strategies we have developed –, that users can select and parametrize This paper offers an overview of the set of features, related, among others, to timbre, tonality, rhythm or form, that can be extracted with the MIRtoolbox One particular analysis is provided as an example The toolbox also includes functions for statistical analysis, segmentation and clustering Particular attention has been paid to the design of a syntax that offers both simplicity of use and transparent adaptiveness to a multiplicity of possible input types Each feature extraction method can accept as argument an audio file, or any preliminary result from intermediary stages of the chain of operations Also the same syntax can be used for analyses of single audio files, batches of files, series of audio segments, multi-channel signals, etc For that purpose, the data and methods of the toolbox are organised in an object-oriented architecture 1 MOTIVATION AND APPROACH MIRtoolbox is a Matlab toolbox dedicated to the extraction of musically-related features from audio recordings It has been designed in particular with the objective of enabling the computation of a large range of features from databases of audio files, that can be subjected to statistical analyses Few softwares have been proposed in this area One particularity of our own approach relies in the use of the Matlab computing environment, which offers good visualisation capabilities and gives access to a large variety of other toolboxes In particular, the MIRtoolbox makes use of functions available in public-domain toolboxes such as the Auditory Toolbox [6], NetLab [5] and SOMtoolbox [10] Other toolboxes, such as the Statistics toolbox or the Neural Network toolbox from MathWorks, can be directly used for further analyses of the features extracted c © 2007 Austrian Computer Society (OCG) by MIRtoolbox without having to export the data from one software to another Such computational framework, because of its general objectives, could be useful to the research community in Music Information Retrieval (MIR), but also for educational purposes For that reason, particular attention has been paid concerning the ease of use of the toolbox In particular, complex analytic processes can be designed using a very simple syntax, whose expressive power comes from the use of an object-oriented paradigm The different musical features extracted from the audio files are highly interdependent: in particular, as can be seen in figure 1, some features are based on the same initial computations In order to improve the computational efficiency, it is important to avoid redundant computations of these common components Each of these intermediary components, and the final musical features, are therefore considered as building blocks that can been freely articulated one with each other Besides, in keeping with the objective of optimal ease of use of the toolbox, each building block has been conceived in a way that it can adapt to the type of input data For instance, the computation of the MFCCs can be based on the waveform of the initial audio signal, or on the intermediary representations such as spectrum, or mel-scale spectrum (see Fig 1) Similarly, autocorrelation is computed for different range of delays depending on the type of input data (audio waveform, envelope, spectrum) This decomposition of all feature extraction algorithms into a common set of building blocks has the advantage of offering a synthetic overview of the different approaches studied in this domain of research 2 FEATURE EXTRACTION 21 Feature overview Figure 1 shows an overview of the main features implemented in the toolbox All the different processes start from the audio signal (on the left) and form a chain of operations proceeding to right Each musical feature is related to one of the musical dimensions traditionally defined in music theory Boldface characters highlight features related to pitch and tonality Bold italics indicate features related to rhythm Simple italics highlight a large set of features that can be associated to timbre and dynamics Among them, all the operators in grey italics can be Audio signal waveform Zero-crossing rate RMS energy Envelope Low Energy Rate Attack Slope Attack Time Envelope Autocorrelation Tempo Onsets

/pdf/mir-in-matlab-ii-a-toolbox-for-musical-feature-extraction-1b88seukjd.pdf

MIR in Matlab (II): A Toolbox for Musical Feature Extraction from Audio.

A Matlab Toolbox for Musical Feature Extraction from Audio

We present MIRToolbox, an integrated set of functions written in Matlab, dedicated to the extraction from audio files of musical features related, among others, to timbre, tonality, rhythm or form. The objective is to offer a state of the art of computational approaches in the area of Music Information Retrieval (MIR). The design is based on a modular framework: the different algorithms are decomposed into stages, formalized using a minimal set of elementary mechanisms, and integrating different variants proposed by alternative approaches — including new strategies we have developed —, that users can select and parametrize. These functions can adapt to a large area of objects as input.

A Matlab Toolbox for Music Information Retrieval

https://neuroaestheticsnet.files.wordpress.com/2012/06/alluri-2012.pdf

Large-scale brain networks emerge from dynamic processing of musical timbre, key and rhythm.

Listening to music often is associated with spontaneous body movements frequently synchronized with its periodic structure. The notion of embodied cognition assumes that intelligent behavior does not emerge from mere passive perception, but requires goal-directed interactions between the organism and its environment. According to this view, one could postulate that we may use our bodily movements to help parse the metric structure of music. The aim of this study was to investigate how pulsations on different metrical levels manifest in music-induced movement. Musicians were presented with a piece of instrumental music in 4/4 time, played at four different tempi ranging from 92 to 138 bpm. Participants were instructed to move to the music, and their movements were recorded with a high quality optical motion capture system. Subsequently, signal processing methods and principal components analysis were applied to extract movement primitives synchronized with different metrical levels. We found differences between metric levels in terms of the prevalence of synchronized eigenmovements. For instance, mediolateral movements of arms were found to be frequently synchronized with the tactus level pulse, while rotation and lateral flexion of the upper torso were commonly found to exhibit periods of two and four beats, respectively. The results imply that periodicities on several metric levels are simultaneously present in music-induced movement. This could suggest that the metric structure of music is encoded in these movements.

/pdf/embodied-meter-hierarchical-eigenmodes-in-music-induced-4p24qd35yu.pdf

Petri Toiviainen

Papers

MIR in Matlab (II): A Toolbox for Musical Feature Extraction from Audio.

A Matlab Toolbox for Musical Feature Extraction from Audio

A Matlab Toolbox for Music Information Retrieval

Large-scale brain networks emerge from dynamic processing of musical timbre, key and rhythm.

Embodied Meter: Hierarchical Eigenmodes in Music-Induced Movement