852 research outputs found
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
A Framework for Bioacoustic Vocalization Analysis Using Hidden Markov Models
Using Hidden Markov Models (HMMs) as a recognition framework for automatic classification of animal vocalizations has a number of benefits, including the ability to handle duration variability through nonlinear time alignment, the ability to incorporate complex language or recognition constraints, and easy extendibility to continuous recognition and detection domains. In this work, we apply HMMs to several different species and bioacoustic tasks using generalized spectral features that can be easily adjusted across species and HMM network topologies suited to each task. This experimental work includes a simple call type classification task using one HMM per vocalization for repertoire analysis of Asian elephants, a language-constrained song recognition task using syllable models as base units for ortolan bunting vocalizations, and a stress stimulus differentiation task in poultry vocalizations using a non-sequential model via a one-state HMM with Gaussian mixtures. Results show strong performance across all tasks and illustrate the flexibility of the HMM framework for a variety of species, vocalization types, and analysis tasks
A Comparison of Front-Ends for Bitstream-Based ASR over IP
Automatic speech recognition (ASR) is called to play a relevant role in the provision of spoken interfaces for IP-based applications. However, as a consequence of the transit of the speech signal over these particular networks, ASR systems need to face two new challenges: the impoverishment of the speech quality due to the compression needed to fit the channel capacity and the inevitable occurrence of packet losses.
In this framework, bitstream-based approaches that obtain the ASR feature vectors directly from the coded bitstream, avoiding the speech decoding process, have been proposed ([S.H. Choi, H.K. Kim, H.S. Lee, Speech recognition using quantized LSP parameters and their transformations in digital communications, Speech Commun. 30 (4) (2000) 223–233. A. Gallardo-AntolÃn, C. Pelà ez-Moreno, F. DÃaz-de-MarÃa, Recognizing GSM digital speech, IEEE Trans. Speech Audio Process., to appear. H.K. Kim, R.V. Cox, R.C. Rose, Performance improvement of a bitstream-based front-end for wireless speech recognition in adverse environments, IEEE Trans. Speech Audio Process. 10 (8) (2002) 591–604. C. Peláez-Moreno, A. Gallardo-AntolÃn, F. DÃaz-de-MarÃa, Recognizing voice over IP networks: a robust front-end for speech recognition on the WWW, IEEE Trans. Multimedia 3(2) (2001) 209–218], among others) to improve the robustness of ASR systems. LSP (Line Spectral Pairs) are the preferred set of parameters for the description of the speech spectral envelope in most of the modern speech coders. Nevertheless, LSP have proved to be unsuitable for ASR, and they must be transformed into cepstrum-type parameters. In this paper we comparatively evaluate the robustness of the most significant LSP to cepstrum transformations in a simulated VoIP (voice over IP) environment which includes two of the most popular codecs used in that network (G.723.1 and G.729) and several network conditions. In particular, we compare ‘pseudocepstrum’ [H.K. Kim, S.H. Choi, H.S. Lee, On approximating Line Spectral Frequencies to LPC cepstral coefficients, IEEE Trans. Speech Audio Process. 8 (2) (2000) 195–199], an approximated but straightforward transformation of LSP into LP cepstral coefficients, with a more computationally demanding but exact one. Our results show that pseudocepstrum is preferable when network conditions are good or computational resources low, while the exact procedure is recommended when network conditions become more adverse.Publicad
A Framework for Bioacoustic Vocalization Analysis Using Hidden Markov Models
Using Hidden Markov Models (HMMs) as a recognition framework for automatic classification of animal vocalizations has a number of benefits, including the ability to handle duration variability through nonlinear time alignment, the ability to incorporate complex language or recognition constraints, and easy extendibility to continuous recognition and detection domains. In this work, we apply HMMs to several different species and bioacoustic tasks using generalized spectral features that can be easily adjusted across species and HMM network topologies suited to each task. This experimental work includes a simple call type classification task using one HMM per vocalization for repertoire analysis of Asian elephants, a language-constrained song recognition task using syllable models as base units for ortolan bunting vocalizations, and a stress stimulus differentiation task in poultry vocalizations using a non-sequential model via a one-state HMM with Gaussian mixtures. Results show strong performance across all tasks and illustrate the flexibility of the HMM framework for a variety of species, vocalization types, and analysis tasks
Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning
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