http://asmp.eurasipjournals.com The latest research articles published by EURASIP Journal on Audio, Speech, and Music Processing 2013-06-18T00:00:00Z Digital audio has been ubiquitous over the past decade. Since it can be easily modified by editing tools, there has been a strong need to protect its content for secure multimedia applications. Previous audio authentication algorithms are mainly focused on either human speech or general audio with music as part of the test data, while special research on music authentication has been somewhat neglected. In this article, we propose a novel algorithm to protect the integrity and authenticity of music signals. Its main contributions include the following: (1) Music is segmented into beat-based frames, which not only endows the authentication units with more semantic meaning but also perfectly resolves the challenging synchronization problem. (2) Robust hashes are generated from chroma-based mid-level audio feature which can appropriately characterize the music content and integrated with an encryption procedure to ensure the security against malicious block-wise vector quantization attack. (3) Fuzzy logic is adopted to make the authentication decision in the light of three measures defined on bit errors, coinciding with the inherent blurred nature of authentication. The experiments exhibit good discriminative ability between admissible and malicious operations. http://asmp.eurasipjournals.com/content/2013/1/11 Wei Li Xiu Zhang Zhurong Wang EURASIP Journal on Audio, Speech, and Music Processing 2013, null:11 2013-06-18T00:00:00Z doi:10.1186/1687-4722-2013-11 /content/figures/1687-4722-2013-11-toc.gif EURASIP Journal on Audio, Speech, and Music Processing 1687-4722 ${item.volume} 11 2013-06-18T00:00:00Z PDF A novel speech bandwidth extension method based on audio watermark is presented in this paper. The time-domain and frequency-domain envelope parameters are extracted from the high-frequency components of speech signal, and then these parameters are embedded in the corresponding narrowband speech bit stream by the modified least significant bit watermark method which uses perception property. At the decoder, the wideband speech is reproduced with the reconstruction of high-frequency components based on the parameters extracted from bit stream of the narrowband speech. The proposed method can decrease poor auditory effect caused by large local distortion. The simulation results show that the synthesized wideband speech has low spectral distortion and its speech perception quality is greatly improved. http://asmp.eurasipjournals.com/content/2013/1/10 Zhe Chen Chengyong Zhao Guosheng Geng Fuliang Yin EURASIP Journal on Audio, Speech, and Music Processing 2013, null:10 2013-06-06T00:00:00Z doi:10.1186/1687-4722-2013-10 /content/figures/1687-4722-2013-10-toc.gif EURASIP Journal on Audio, Speech, and Music Processing 1687-4722 ${item.volume} 10 2013-06-06T00:00:00Z PDF This paper presents a novel lossless compression technique of the context-based adaptive arithmetic coding which can be used to further compress the quantized parameters in audio codec. The key feature of the new technique is the combination of the context model in time domain and frequency domain which is called time-frequency context model. It is used for the lossless compression of audio coding parameters such as the quantized modified discrete cosine transform (MDCT) coefficients and the frequency band gains in ITU-T G.719 audio codec. With the proposed adaptive arithmetic coding, a high degree of adaptation and redundancy reduction can be achieved. In addition, an efficient variable rate algorithm is employed, which is designed based on both the baseline entropy coding method of G.719 and the proposed adaptive arithmetic coding technique. Experiments show that the proposed technique is of higher efficiency compared with the conventional Huffman coding and the common adaptive arithmetic coding when used in the lossless compression of audio coding parameters. For a set of audio samples used in the G.719 application, the proposed technique achieves an average bit rate saving of 7.2% at low bit rate coding mode while producing audio quality equal to that of the original G.719. http://asmp.eurasipjournals.com/content/2013/1/9 Jing Wang Xuan Ji Shenghui Zhao Xiang Xie Jingming Kuang EURASIP Journal on Audio, Speech, and Music Processing 2013, null:9 2013-05-21T00:00:00Z doi:10.1186/1687-4722-2013-9 /content/figures/1687-4722-2013-9-toc.gif EURASIP Journal on Audio, Speech, and Music Processing 1687-4722 ${item.volume} 9 2013-05-21T00:00:00Z XML This article analyzes and compares influence of different types of spectral and prosodic features for Czech and Slovak emotional speech classification based on Gaussian mixture models (GMM). Influence of initial setting of parameters (number of mixture components and used number of iterations) for GMM training process was analyzed, too. Subsequently, analysis was performed to find how correctness of emotion classification depends on the number and the order of the parameters in the input feature vector and on the computation complexity. Another test was carried out to verify the functionality of the proposed two-level architecture comprising the gender recognizer and of the emotional speech classifier. Next tests were realized to find dependence of some negative aspect (processing of the input speech signal with too short time duration, the gender of a speaker incorrectly determined, etc.) on the stability of the results generated during the GMM classification process. Evaluations and tests were realized with the speech material in the form of sentences of male and female speakers expressing four emotional states (joy, sadness, anger, and a neutral state) in Czech and Slovak languages. In addition, a comparative experiment using the speech data corpus in other language (German) was performed. The mean classification error rate of the whole classifier structure achieves about 21% for all four emotions and both genders, and the best obtained error rate was 3.5% for the sadness style of the female gender. These values are acceptable in this first stage of development of the GMM classifier. On the other hand, the test showed the principal importance of correct classification of the speaker gender in the first level, which has heavy influence on the resulting recognition score of the emotion classification. This GMM classifier should be used for evaluation of the synthetic speech quality after applied voice conversion and emotional speech style transformation. http://asmp.eurasipjournals.com/content/2013/1/8 Ji¿í P¿ibil Anna P¿ibilová EURASIP Journal on Audio, Speech, and Music Processing 2013, null:8 2013-04-24T00:00:00Z doi:10.1186/1687-4722-2013-8 /content/figures/1687-4722-2013-8-toc.gif EURASIP Journal on Audio, Speech, and Music Processing 1687-4722 ${item.volume} 8 2013-04-24T00:00:00Z XML In this paper, we describe a speaker adaptation method based on the probabilistic 2-mode analysis of training models. Probabilistic 2-mode analysis is a probabilistic extension of multilinear analysis. We apply probabilistic 2-mode analysis to speaker adaptation by representing each of the hidden Markov model mean vectors of training speakers as a matrix, and derive the speaker adaptation equation in the maximum a posteriori (MAP) framework. The adaptation equation becomes similar to the speaker adaptation equation using the MAP linear regression adaptation. In the experiments, the adapted models based on probabilistic 2-mode analysis showed performance improvement over the adapted models based on Tucker decomposition, which is a representative multilinear decomposition technique, for small amounts of adaptation data while maintaining good performance for large amounts of adaptation data. http://asmp.eurasipjournals.com/content/2013/1/7 Yongwon Jeong EURASIP Journal on Audio, Speech, and Music Processing 2013, null:7 2013-04-11T00:00:00Z doi:10.1186/1687-4722-2013-7 /content/figures/1687-4722-2013-7-toc.gif EURASIP Journal on Audio, Speech, and Music Processing 1687-4722 ${item.volume} 7 2013-04-11T00:00:00Z PDF Availability of large amounts of raw unlabeled data has sparked the recent surge in semi-supervised learning research. In most works, however, it is assumed that labeled and unlabeled data come from the same distribution. This restriction is removed in the self-taught learning algorithm where unlabeled data can be different, but nevertheless have similar structure. First, a representation is learned from the unlabeled samples by decomposing their data matrix into two matrices called bases and activations matrix respectively. This procedure is justified by the assumption that each sample is a linear combination of the columns in the bases matrix which can be viewed as high level features representing the knowledge learned from the unlabeled data in an unsupervised way. Next, activations of the labeled data are obtained using the bases which are kept fixed. Finally, a classifier is built using these activations instead of the original labeled data. In this work, we investigated the performance of three popular methods for matrix decomposition: Principal Component Analysis (PCA), Non-negative Matrix Factorization (NMF) and Sparse Coding (SC) as unsupervised high level feature extractors for the self-taught learning algorithm. We implemented this algorithm for the music genre classification task using two different databases: one as unlabeled data pool and the other as data for supervised classifier training. Music pieces come from 10 and 6 genres for each database respectively, while only one genre is common for the both of them. Results from wide variety of experimental settings show that the self-taught learning method improves the classification rate when the amount of labeled data is small and, more interestingly, that consistent improvement can be achieved for a wide range of unlabeled data sizes. The best performance among the matrix decomposition approaches was shown by the Sparse Coding method. http://asmp.eurasipjournals.com/content/2013/1/6 Konstantin Markov Tomoko Matsui EURASIP Journal on Audio, Speech, and Music Processing 2013, null:6 2013-04-09T00:00:00Z doi:10.1186/1687-4722-2013-6 /content/figures/1687-4722-2013-6-toc.gif EURASIP Journal on Audio, Speech, and Music Processing 1687-4722 ${item.volume} 6 2013-04-09T00:00:00Z XML A comprehensive system for facial animation of generic 3D head models driven by speech is presentedin this article. In the training stage, audio-visual information is extracted from audio-visualtraining data, and then used to compute the parameters of a single joint audio-visual hidden Markovmodel (AV-HMM). In contrast to most of the methods in the literature, the proposed approach doesnot require segmentation/classification processing stages of the audio-visual data, avoiding the errorpropagation related to these procedures. The trained AV-HMM provides a compact representation ofthe audio-visual data, without the need of phoneme (word) segmentation, which makes it adaptableto different languages. Visual features are estimated from the speech signal based on the inversionof the AV-HMM. The estimated visual speech features are used to animate a simple face model. Theanimation of a more complex head model is then obtained by automatically mapping the deformationof the simple model to it, using a small number of control points for the interpolation. The proposedalgorithm allows the animation of 3D head models of arbitrary complexity through a simple setupprocedure. The resulting animation is evaluated in terms of intelligibility of visual speech throughperceptual tests, showing a promising performance. The computational complexity of the proposedsystem is analyzed, showing the feasibility of its real-time implementation. http://asmp.eurasipjournals.com/content/2013/1/5 Lucas Terissi Mauricio Cerda Juan Gómez Nancy Hitschfeld-Kahler Bernard Girau EURASIP Journal on Audio, Speech, and Music Processing 2013, null:5 2013-02-01T00:00:00Z doi:10.1186/1687-4722-2013-5 /content/figures/1687-4722-2013-5-toc.gif EURASIP Journal on Audio, Speech, and Music Processing 1687-4722 ${item.volume} 5 2013-02-01T00:00:00Z PDF Blind source separation (BSS) and sound activity detection (SAD) from a sound source mixture with minimum prior information are two major requirements for computational auditory scene analysis that recognizes auditory events in many environments. In daily environments, BSS suffers from many problems such as reverberation, a permutation problem in frequency-domain processing, and uncertainty about the number of sources in the observed mixture. While many conventional BSS methods resort to a cascaded combination of subprocesses, e.g., frequency-wise separation and permutation resolution, to overcome these problems, their outcomes may be affected by the worst subprocess. Our aim is to develop a unified framework to cope with these problems. Our method, called permutation-free infinite sparse factor analysis (PF-ISFA), is based on a nonparametric Bayesian framework that enables inference without a pre-determined number of sources. It solves BSS, SAD and the permutation problem at the same time. Our method has two key ideas: unified source activities for all the frequency bins and the activation probabilities of all the frequency bins of all the sources. Experiments were carried out to evaluate the separation performance and the SAD performance under four reverberant conditions. For separation performance in the BSS_EVAL criteria, our method outperformed conventional complex ISFA under all conditions. For SAD performance, our method outperformed the conventional method by 5.9–0.5% in F-measure under the condition RT20 = 30–600 [ms], respectively. http://asmp.eurasipjournals.com/content/2013/1/4 Kohei Nagira Takuma Otsuka Hiroshi Okuno EURASIP Journal on Audio, Speech, and Music Processing 2013, null:4 2013-01-22T00:00:00Z doi:10.1186/1687-4722-2013-4 /content/figures/1687-4722-2013-4-toc.gif EURASIP Journal on Audio, Speech, and Music Processing 1687-4722 ${item.volume} 4 2013-01-22T00:00:00Z XML We propose an efficient solution to the problem of sparse linear prediction analysis of the speech signal. Our method is based on minimization of a weighted l 2-norm of the prediction error. The weighting function is constructed such that less emphasis is given to the error around the points where we expect the largest prediction errors to occur (the glottal closure instants) and hence the resulting cost function approaches the ideal l 0-norm cost function for sparse residual recovery. We show that the efficient minimization of this objective function (by solving normal equations of linear least squares problem) provides enhanced sparsity level of residuals compared to the l 1-norm minimization approach which uses the computationally demanding convex optimization methods. Indeed, the computational complexity of the proposed method is roughly the same as the classic minimum variance linear prediction analysis approach. Moreover, to show a potential application of such sparse representation, we use the resulting linear prediction coefficients inside a multi-pulse synthesizer and show that the corresponding multi-pulse estimate of the excitation source results in slightly better synthesis quality when compared to the classical technique which uses the traditional non-sparse minimum variance synthesizer. http://asmp.eurasipjournals.com/content/2013/1/3 Vahid Khanagha Khalid Daoudi EURASIP Journal on Audio, Speech, and Music Processing 2013, null:3 2013-01-22T00:00:00Z doi:10.1186/1687-4722-2013-3 /content/figures/1687-4722-2013-3-toc.gif EURASIP Journal on Audio, Speech, and Music Processing 1687-4722 ${item.volume} 3 2013-01-22T00:00:00Z XML A lot of effort has been made in Computational Auditory Scene Analysis (CASA) to segregate target speech from monaural mixtures. Based on the principle of CASA, this article proposes an improved algorithm for monaural speech segregation. To extract the energy feature more accurately, the proposed algorithm improves the threshold selection for response energy in initial segmentation stage. Since the resulting mask map often contains broken auditory element groups after grouping stage, a smoothing stage is proposed based on morphological image processing. Through the combination of erosion and dilation operations, we suppress the intrusions by removing the unwanted particles and enhance the segregated speech by complementing the broken auditory elements. Systematic evaluation shows that the proposed segregation algorithm improves the output signal-to-noise ratio by an average of 8.55 dB and cuts the percentage of noise residue by an average of 25.36% compared with the mixture, yielding a significant improvement for speech segregation. http://asmp.eurasipjournals.com/content/2013/1/2 Wang Yu Lin Jiajun Chen Ning Yuan Wenhao EURASIP Journal on Audio, Speech, and Music Processing 2013, null:2 2013-01-12T00:00:00Z doi:10.1186/1687-4722-2013-2 /content/figures/1687-4722-2013-2-toc.gif EURASIP Journal on Audio, Speech, and Music Processing 1687-4722 ${item.volume} 2 2013-01-12T00:00:00Z XML