Optimal filtering of dynamics in short-time features for music organization.
ABSTRACT There is an increasing interest in customizable methods for organizing music collections. Relevant music characteriza- tion can be obtained from short-time features, but it is not obvious how to combine them to get useful information. In this work, a novel method, denoted as the Positive Con- strained Orthonormalized Partial Least Squares (POPLS), is proposed. Working on the periodograms of MFCCs time series, this supervised method finds optimal filters which pick up the most discriminative temporal information for any music organization task. Two examples are presented in the paper, the first being a simple proof-of-concept, where an altosax with and without vibrato is modelled. A more complex 11 music genre classification setup is also inves- tigated to illustrate the robustness and validity of the pro - posed method on larger datasets. Both experiments showed the good properties of our method, as well as superior per- formance when compared to a fixed filter bank approach suggested previously in the MIR literature. We think that the proposed method is a natural step towards a customized MIR application that generalizes well to a wide range of dif- ferent music organization tasks.
Full-textDOI: · Available from: Anders Meng, Feb 09, 2015
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Article: Classification in music research[Show abstract] [Hide abstract]
ABSTRACT: Since a few years, classification in music research is a very broad and quickly growing field. Most important for adequate classification is the knowledge of adequate observable or deduced features on the basis of which meaningful groups or classes can be distinguished. Unsupervised classification additionally needs an adequate similarity or distance measure grouping is to be based upon. Evaluation of supervised learning is typically based on the error rates of the classification rules. In this paper we first discuss typical problems and possible influential features derived from signal analysis, mental mechanisms or concepts, and compositional structure. Then, we present typical solutions of such tasks related to music research, namely for organization of music collections, transcription of music signals, cognitive psychology of music, and compositional structure analysis.Advances in Data Analysis and Classification 11/2007; 1(3):255-291. DOI:10.1007/s11634-007-0016-x · 0.92 Impact Factor
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ABSTRACT: There is an increasing interest in customizable methods for organizing music col- lections. Relevant music characterization can be obtained from short-time fea- tures, but it is not obvious how to combine them to get useful information. First, the relevant information might not be evident at the short-t ime level, and these features have to be combined at a larger temporal level into a new feature vector in order to capture the relevant information. Second, we need to learn a model for the new features that generalizes well to new data. In thi s contribution, we will study how multivariate analysis (MVA) and kernel methods can be of great help in this task. More precisely, we will present two modifie d versions of a MVA method known as Orthonormalized Partial Least Squares (OPLS), one of them being a kernel extension, that are well-suited for discover ing relevant dynamics in large music collections. The performance of both schemes will be illustrated in a music genre classification task.
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ABSTRACT: The identification of acoustic events produced in meeting-rooms or classrooms could help to identify and to describe social and human activities that take place on these rooms. This article focus on the classification of 20 acoustic events, using short-time acoustic features based on MFCC (Mel-Frequency Cepstral Coefficients), applied on three different time integration techniques, and using a classifier based on SVM (Support Vector Machine). We analyze the performance obtained with each one of these techniques to eventually combine them with the aim of improving overall performance of the classification system.