1,772 research outputs found
Classification of music genres using sparse representations in overcomplete dictionaries
This paper presents a simple, but efficient and robust, method for music genre classification that utilizes sparse representations in overcomplete dictionaries. The training step involves creating dictionaries, using the K-SVD algorithm, in which data corresponding to a particular music genre has a sparse representation. In the classification step, the Orthogonal Matching Pursuit (OMP) algorithm is used to separate feature vectors that consist only of Linear Predictive Coding (LPC) coefficients. The paper analyses in detail a popular case study from the literature, the ISMIR 2004 database. Using the presented method, the correct classification percentage of the 6 music genres is 85.59, result that is comparable with the best results published so far
A Deep Representation for Invariance And Music Classification
Representations in the auditory cortex might be based on mechanisms similar
to the visual ventral stream; modules for building invariance to
transformations and multiple layers for compositionality and selectivity. In
this paper we propose the use of such computational modules for extracting
invariant and discriminative audio representations. Building on a theory of
invariance in hierarchical architectures, we propose a novel, mid-level
representation for acoustical signals, using the empirical distributions of
projections on a set of templates and their transformations. Under the
assumption that, by construction, this dictionary of templates is composed from
similar classes, and samples the orbit of variance-inducing signal
transformations (such as shift and scale), the resulting signature is
theoretically guaranteed to be unique, invariant to transformations and stable
to deformations. Modules of projection and pooling can then constitute layers
of deep networks, for learning composite representations. We present the main
theoretical and computational aspects of a framework for unsupervised learning
of invariant audio representations, empirically evaluated on music genre
classification.Comment: 5 pages, CBMM Memo No. 002, (to appear) IEEE 2014 International
Conference on Acoustics, Speech, and Signal Processing (ICASSP 2014
Beat histogram features for rhythm-based musical genre classification using multiple novelty functions
In this paper we present beat histogram features for multiple level rhythm description and evaluate them in a musical genre classification task. Audio features pertaining to various musical content categories and their related novelty functions are extracted as a basis for the creation of beat histograms. The proposed features capture not only amplitude, but also tonal and general spectral changes in the signal, aiming to represent as much rhythmic information as possible. The most and least informative features are identified through feature selection methods and are then tested using Support Vector Machines on five genre datasets concerning classification accuracy against a baseline feature set. Results show that the presented features provide comparable classification accuracy with respect to other genre classification approaches using periodicity histograms and display a performance close to that of much more elaborate up-to-date approaches for rhythm description. The use of bar boundary annotations for the texture frames has provided an improvement for the dance-oriented Ballroom dataset. The comparably small number of descriptors and the possibility of evaluating the influence of specific signal components to the general rhythmic content encourage the further use of the method in rhythm description tasks
The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use
The GTZAN dataset appears in at least 100 published works, and is the
most-used public dataset for evaluation in machine listening research for music
genre recognition (MGR). Our recent work, however, shows GTZAN has several
faults (repetitions, mislabelings, and distortions), which challenge the
interpretability of any result derived using it. In this article, we disprove
the claims that all MGR systems are affected in the same ways by these faults,
and that the performances of MGR systems in GTZAN are still meaningfully
comparable since they all face the same faults. We identify and analyze the
contents of GTZAN, and provide a catalog of its faults. We review how GTZAN has
been used in MGR research, and find few indications that its faults have been
known and considered. Finally, we rigorously study the effects of its faults on
evaluating five different MGR systems. The lesson is not to banish GTZAN, but
to use it with consideration of its contents.Comment: 29 pages, 7 figures, 6 tables, 128 reference
INSTRUMENTATION-BASED MUSIC SIMILARITY USING SPARSE REPRESENTATIONS
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