3rd European Conference on Speech Communication and Technology (Eurospeech 1993)
This paper describes the continuous speech recognition subsystem "ATREUS" which is used... more This paper describes the continuous speech recognition subsystem "ATREUS" which is used as the speech input stage in the experimental speech translation system "ASURA." The speech recognition algorithm is SSS-LR/VFS which consists of context-dependent phone models (HMnet), a generalized LR parser, and vector field smoothing for speaker / environment adaptation.
6th International Conference on Spoken Language Processing (ICSLP 2000)
A sharable software repository for Japanese LVCSR (Large Vocabulary Continuous Speech Recognition... more A sharable software repository for Japanese LVCSR (Large Vocabulary Continuous Speech Recognition) is introduced. It is designed as a baseline platform for research and developed by researchers of different academic institutes under a governmental support. The repository consists of a recognition engine (Julius), Japanese acoustic models and statistical language models as well as Japanese morphological analysis tools. These modules can be easily integrated and replaced under a plug-and-play framework, which makes it possible to fairly evaluate components and to develop specific application systems. Assessment of these modules and systems in a 20000-word dictation task is reported. The software repository is freely available to the public.
In this paper, we present a real-time equalizer to control a volume balance of harmonic and percu... more In this paper, we present a real-time equalizer to control a volume balance of harmonic and percussive components in music signals without a priori knowledge of scores or included instruments. The harmonic and percussive components of music signals have much different structures in the power spectrogram domain, the former is horizontal, while the latter is vertical. Exploiting the anisotropy, our methods separate input music signals into them based on the MAP estimation framework. We derive two kind of algorithm based on a I-divergence-based mixing model and a hard mixing model. Although they include iterative update equations, we realized the real-time processing by a sliding analysis technique. The separated harmonic and percussive components are finally remixed in an arbitrary volume balance and played. We show the prototype system implemented on Windows environment.
This paper presents a technique for estimating HMM model parameters for noisy speech from given c... more This paper presents a technique for estimating HMM model parameters for noisy speech from given clean speech HMM and noise HMM. The model parameters are estimated by approximating the non-linear function governing the relationship between speech and noise, by a Lagrange polynomial, and thus enabling the distribution of corrupted speech parameters to have a closed form. The method is computationally efficient, and the experimental results showed significant improvement in recognition performance of noisy speech with this approach. Typically, word accuracy increased from 9.2% with clean model to 82.8% with the model composed by the proposed method as compared to 45.4% with the model composed by PMC Log-normal approximation, on an isolated word recognition task for exhibition hall noise added at 10 dB SNR.
This paper describes a multi-pitch analysis method using specmurt analysis with iterative estimat... more This paper describes a multi-pitch analysis method using specmurt analysis with iterative estimation of the quasioptimal common harmonic structure function. Specmurt analysis is based upon the idea that superimposed harmonic structure pattern can be expressed as a convolution of two components, a fundamental frequency distribution and a 'common harmonic structure' function if each underlying tone component has similar harmonic structure pattern. As proved in our previous work inappropriate common structure function leads to inaccurate analysis results. The iterative algorithm proposed in this paper automatically chooses a proper structure, which results in finding concurrent multiple fundamental frequencies and reduces the dependency on heuristically chosen initial common harmonic structure. The experimental evaluation showed promising results.
This paper introduces our ongoing work on generative modeling of speech fundamental frequency (F0... more This paper introduces our ongoing work on generative modeling of speech fundamental frequency (F0) contours for estimating prosodic features from raw speech data. The present F 0 contour model is formulated by translating the Fujisaki model, a well-founded mathematical model representing the control mechanism of vocal fold vibration, into a probabilistic model described as a discrete-time stochastic process. The motivation behind this formulation is two fold. One is to derive a general parameter estimation framework for the Fujisaki model, allowing for the introduction of powerful statistical methods. The other is to construct an automatically trainable version of the Fujisaki model so that in future it can be used to develop a statistical speaking style conversion system or incorporated into existing text-to-speech synthesis systems to improve the naturalness and intelligibility of computer-generated speech. We also briefly introduce a generative model of F 0 contours of singing voice developed under the same spirit.
In this paper, we propose harmony generation according to user input parameters based on fundamen... more In this paper, we propose harmony generation according to user input parameters based on fundamental harmonic properties as a new approach to the problem of automatic music completion (the automatic generation of music pieces from any incomplete fragments of music), which we have proposed as a generalization of conventional music information problems such as automatic melody generation and harmonization. The goal is enabling possibly inexperienced users to turn partial musical ideas into complete pieces for quick exploration of musical possibilities. Therefore, the focus lies on response to intuitive modes of input, allowing the user to intentionally shape the generated music. To that end, parameterized harmony generation utilizes fundamental musical principles which are understandable by both user and computer, instead of conventional probabilistic models (which imply imitation of a style or data corpus) or restrictive rule-based models. We apply this approach to the automatic completion of four-part chorales, using the harmonic concepts of active tones, cadences and key modulation. We implemented a system that jointly optimizes harmony and voicing considering both user input and music theory. Our system was evaluated by a professional composer and in a subjective evaluation experiment. We also invite the reader to use our system at music completion.
In this paper, we propose and discuss Composite Wavelet Model (CWM) for speech analysis and synth... more In this paper, we propose and discuss Composite Wavelet Model (CWM) for speech analysis and synthesis. Conventional speech synthesis using recursive filter involves a problem of temporal characteristics which may cause degradation of synthetic speech quality. In our new method, speech is analyzed by approximating the spectral envelope with a Gaussian Mixture Model (GMM). In the synthesis stage, the basic waveform is chosen to be composite Gabor function which is inverse Fourier transform of Gaussian mixture and is pitch-synchronously overlapped and added. For preliminary evaluation of the proposed method, we analyzed and synthesized speech and showed that it improved the temporal characteristics.
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010
Estimation of melody line in homophonic music audio signals is a challenging subject of study. So... more Estimation of melody line in homophonic music audio signals is a challenging subject of study. Some of the difficulties are derived from presence of accompanying components. To overcome those difficulties, we propose a method to enhance melodic components in music audio signals. The enhancement algorithm uses fluctuation and shortness of melodic components, which we call temporal-variability. We also discuss a melody tracking algorithm, which can be simple thanks to the preprocessing. In this paper, we describe the enhancement method and tracking method, and show the experimental results that supports the efficiency of our methods.
Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.
This paper describes a method of generating a Kanji hierarchical structured dictionary for stroke... more This paper describes a method of generating a Kanji hierarchical structured dictionary for stroke-number and stroke-order free handwriting recognition based on substroke HMM. In stroke-based methods, a large number of stroke-order variations can be easily expressed by just adding different stroke sequences to the dictionary and it is not necessary to train new reference patterns. The hierarchical structured dictionary has an advantage that thousands of stroke-order variations of Kanji characters can be produced using a small number of stroke-order rules defining Kanji parts. Moreover, the recognition speed is fast since common sequences are shared in a substroke network, even if the total number of stroke-order combinations becomes enormous practically. In experiments, 300 different stroke-order rules of Kanji parts were statistically chosen by using 60 writers' handwritings of 1,016 educational Kanji characters. By adding these new stroke-order rules to the dictionary, about 9,000 variations of different stroke-orders were generated for 2,965 JIS 1st level Kanji characters. As a result, we successfully improved the recognition accuracy from 82.6% to 90.2% for stroke-order free handwritings.
Speech, Sound and Music Processing: Embracing Research in India, 2012
HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific r... more HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
We propose a new statistical model, named Hierarchical Topic Trajectory Model (HTTM), for acquiri... more We propose a new statistical model, named Hierarchical Topic Trajectory Model (HTTM), for acquiring a dynamically changing topic model that represents the relationship between video frames and associated text labels. Model parameter estimation, annotation and retrieval can be executed within a unified framework with a few computation. It is also easy to add new modals such as audio signal and geotags. Preliminary experiments on video annotation task with manually annotated video dataset indicate that our proposed method can improve the annotation accuracy.
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
This paper presents a new sparse representation for acoustic signals which is based on a mixing m... more This paper presents a new sparse representation for acoustic signals which is based on a mixing model defined in the complex-spectrum domain (where additivity holds), and allows us to extract recurrent patterns of magnitude spectra that underlie observed complex spectra and the phase estimates of constituent signals. An efficient iterative algorithm is derived, which reduces to the multiplicative update algorithm for non-negative matrix factorization developed by Lee under a particular condition.
Wiener filtering is one of the most widely used methods in audio source separation. It is often a... more Wiener filtering is one of the most widely used methods in audio source separation. It is often applied on time-frequency representations of signals, such as the short-time Fourier transform (STFT), to exploit their short-term stationarity, but so far the design of the Wiener time-frequency mask did not take into account the necessity for the output spectrograms to be consistent, i.e., to correspond to the STFT of a time-domain signal. In this paper, we generalize the concept of Wiener filtering to time-frequency masks which can involve manipulation of the phase as well by formulating the problem as a consistency-constrained Maximum-Likelihood one. We present two methods to solve the problem, one looking for the optimal time-domain signal, the other promoting consistency through a penalty function directly in the time-frequency domain. We show through experimental evaluation that, both in oracle conditions and combined with spectral subtraction, our method outperforms classical Wiener filtering.
In this paper, a co-channel multi-pitch detection algorithm is described. We suggest the importan... more In this paper, a co-channel multi-pitch detection algorithm is described. We suggest the importance of this when prosodic information is need to be extracted separately from respective F 0 patterns of concurrent utterances. Though temporal continuity of speech prosody should be considered, we discuss a process done independently on each single frame as the first step. A model of multiple harmonic structures is constructed with a mixture of tied Gaussian mixtures with which a single harmonic structure is modeled. Our algorithm enables to detect both a number of concurrent speakers, and each spectral envelope of underlying harmonic structure based on a maximum likelihood estimation of the model parameters using EM algorithm and an information criterion. It operates without a priori information of F 0 contours and a restriction of a number of speakers, and it also extracts accurate F 0 s as continuous values with simple procedures in spectral domain. Experiments showed our algorithm outperformed well-known cepstrum for both speech signals of a single speaker and simultaneous two speakers.
This paper proposes a Hidden Markov Model (HMM)-based algorithm for automatic decision of piano f... more This paper proposes a Hidden Markov Model (HMM)-based algorithm for automatic decision of piano fingering. We represent the positions and forms of hands and fingers as HMM states and model the resulted sequence of performed notes as emissions associated with HMM transitions. Optimal fingering decision is thus formulated as Viterbi search to find the most likely sequence of state transitions. The proposed algorithm models the required efforts in pressing a key with a finger followed by another key with another finger, and in two-dimensional positioning of fingers on the piano keyboard with diatonic and chromatic keys. Fundamental functionality of the algorithm was verified through experiments with real piano pieces. This framework can be extended to polyphonic music containing chords.
ABSTRACT This extended abstract details submissions to the Music Information Retrieval Evaluation... more ABSTRACT This extended abstract details submissions to the Music Information Retrieval Evaluation eXchange. Three or four feature sets based on musical knowledge [1] were cal-culated. Then an SVM with the ANOVA RBF kernel was used, or a canonical LDA was conducted with effective features chosen from the sets. AIC values or accuracy of cross validation of training data were calculated for the feature selection. Four submissions were made by com-binations of feature sets and classifiers.
2009 IEEE International Conference on Multimedia and Expo, 2009
Many musical genres and styles are characterized by distinct representative rhythmic patterns. In... more Many musical genres and styles are characterized by distinct representative rhythmic patterns. In most automatic genre classification systems global statistical features based on timbral dynamics such as Mel-Frequency Cepstral Coefficients (MFCC) are utilized but so far rhythmic information has not so effectively been used. In order to extract bar-long unit rhythmic patterns for a music collection we propose a clustering method based on one-pass dynamic programming and k-means clustering. After extracting the fundamental rhythmic patterns for each style/genre a pattern occurrence histogram is calculated and used as a feature vector for supervised learning. Experimental results show that the automatically calculated rhythmic pattern information can be used to effectively classify musical genre/style and improve upon current approaches based on timbral features.
Given a relatively small selection of guitar scores for a large population of guitarists, there s... more Given a relatively small selection of guitar scores for a large population of guitarists, there should be a certain demand for systems that can automatically arrange an arbitrary score for guitars. Our aim in this paper is to formulate the "fingering decision" and "arrangement" in a unified framework that can be cast as a decoding problem of a hidden Markov model (HMM). The left hand forms on the fingerboard are considered as the hidden states and the note sequence of a given score as an observed sequence generated by the HMM. Finding the most likely sequence of the hidden states thus corresponds to performing fingering decision or arrangement. The manual setting of HMM parameters reflecting preference of beginner guitarists lets the framework generate natural fingerings and arrangements suitable for beginners. Some examples of fingering and arrangement produced by the proposed method are presented.
3rd European Conference on Speech Communication and Technology (Eurospeech 1993)
This paper describes the continuous speech recognition subsystem "ATREUS" which is used... more This paper describes the continuous speech recognition subsystem "ATREUS" which is used as the speech input stage in the experimental speech translation system "ASURA." The speech recognition algorithm is SSS-LR/VFS which consists of context-dependent phone models (HMnet), a generalized LR parser, and vector field smoothing for speaker / environment adaptation.
6th International Conference on Spoken Language Processing (ICSLP 2000)
A sharable software repository for Japanese LVCSR (Large Vocabulary Continuous Speech Recognition... more A sharable software repository for Japanese LVCSR (Large Vocabulary Continuous Speech Recognition) is introduced. It is designed as a baseline platform for research and developed by researchers of different academic institutes under a governmental support. The repository consists of a recognition engine (Julius), Japanese acoustic models and statistical language models as well as Japanese morphological analysis tools. These modules can be easily integrated and replaced under a plug-and-play framework, which makes it possible to fairly evaluate components and to develop specific application systems. Assessment of these modules and systems in a 20000-word dictation task is reported. The software repository is freely available to the public.
In this paper, we present a real-time equalizer to control a volume balance of harmonic and percu... more In this paper, we present a real-time equalizer to control a volume balance of harmonic and percussive components in music signals without a priori knowledge of scores or included instruments. The harmonic and percussive components of music signals have much different structures in the power spectrogram domain, the former is horizontal, while the latter is vertical. Exploiting the anisotropy, our methods separate input music signals into them based on the MAP estimation framework. We derive two kind of algorithm based on a I-divergence-based mixing model and a hard mixing model. Although they include iterative update equations, we realized the real-time processing by a sliding analysis technique. The separated harmonic and percussive components are finally remixed in an arbitrary volume balance and played. We show the prototype system implemented on Windows environment.
This paper presents a technique for estimating HMM model parameters for noisy speech from given c... more This paper presents a technique for estimating HMM model parameters for noisy speech from given clean speech HMM and noise HMM. The model parameters are estimated by approximating the non-linear function governing the relationship between speech and noise, by a Lagrange polynomial, and thus enabling the distribution of corrupted speech parameters to have a closed form. The method is computationally efficient, and the experimental results showed significant improvement in recognition performance of noisy speech with this approach. Typically, word accuracy increased from 9.2% with clean model to 82.8% with the model composed by the proposed method as compared to 45.4% with the model composed by PMC Log-normal approximation, on an isolated word recognition task for exhibition hall noise added at 10 dB SNR.
This paper describes a multi-pitch analysis method using specmurt analysis with iterative estimat... more This paper describes a multi-pitch analysis method using specmurt analysis with iterative estimation of the quasioptimal common harmonic structure function. Specmurt analysis is based upon the idea that superimposed harmonic structure pattern can be expressed as a convolution of two components, a fundamental frequency distribution and a 'common harmonic structure' function if each underlying tone component has similar harmonic structure pattern. As proved in our previous work inappropriate common structure function leads to inaccurate analysis results. The iterative algorithm proposed in this paper automatically chooses a proper structure, which results in finding concurrent multiple fundamental frequencies and reduces the dependency on heuristically chosen initial common harmonic structure. The experimental evaluation showed promising results.
This paper introduces our ongoing work on generative modeling of speech fundamental frequency (F0... more This paper introduces our ongoing work on generative modeling of speech fundamental frequency (F0) contours for estimating prosodic features from raw speech data. The present F 0 contour model is formulated by translating the Fujisaki model, a well-founded mathematical model representing the control mechanism of vocal fold vibration, into a probabilistic model described as a discrete-time stochastic process. The motivation behind this formulation is two fold. One is to derive a general parameter estimation framework for the Fujisaki model, allowing for the introduction of powerful statistical methods. The other is to construct an automatically trainable version of the Fujisaki model so that in future it can be used to develop a statistical speaking style conversion system or incorporated into existing text-to-speech synthesis systems to improve the naturalness and intelligibility of computer-generated speech. We also briefly introduce a generative model of F 0 contours of singing voice developed under the same spirit.
In this paper, we propose harmony generation according to user input parameters based on fundamen... more In this paper, we propose harmony generation according to user input parameters based on fundamental harmonic properties as a new approach to the problem of automatic music completion (the automatic generation of music pieces from any incomplete fragments of music), which we have proposed as a generalization of conventional music information problems such as automatic melody generation and harmonization. The goal is enabling possibly inexperienced users to turn partial musical ideas into complete pieces for quick exploration of musical possibilities. Therefore, the focus lies on response to intuitive modes of input, allowing the user to intentionally shape the generated music. To that end, parameterized harmony generation utilizes fundamental musical principles which are understandable by both user and computer, instead of conventional probabilistic models (which imply imitation of a style or data corpus) or restrictive rule-based models. We apply this approach to the automatic completion of four-part chorales, using the harmonic concepts of active tones, cadences and key modulation. We implemented a system that jointly optimizes harmony and voicing considering both user input and music theory. Our system was evaluated by a professional composer and in a subjective evaluation experiment. We also invite the reader to use our system at music completion.
In this paper, we propose and discuss Composite Wavelet Model (CWM) for speech analysis and synth... more In this paper, we propose and discuss Composite Wavelet Model (CWM) for speech analysis and synthesis. Conventional speech synthesis using recursive filter involves a problem of temporal characteristics which may cause degradation of synthetic speech quality. In our new method, speech is analyzed by approximating the spectral envelope with a Gaussian Mixture Model (GMM). In the synthesis stage, the basic waveform is chosen to be composite Gabor function which is inverse Fourier transform of Gaussian mixture and is pitch-synchronously overlapped and added. For preliminary evaluation of the proposed method, we analyzed and synthesized speech and showed that it improved the temporal characteristics.
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010
Estimation of melody line in homophonic music audio signals is a challenging subject of study. So... more Estimation of melody line in homophonic music audio signals is a challenging subject of study. Some of the difficulties are derived from presence of accompanying components. To overcome those difficulties, we propose a method to enhance melodic components in music audio signals. The enhancement algorithm uses fluctuation and shortness of melodic components, which we call temporal-variability. We also discuss a melody tracking algorithm, which can be simple thanks to the preprocessing. In this paper, we describe the enhancement method and tracking method, and show the experimental results that supports the efficiency of our methods.
Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.
This paper describes a method of generating a Kanji hierarchical structured dictionary for stroke... more This paper describes a method of generating a Kanji hierarchical structured dictionary for stroke-number and stroke-order free handwriting recognition based on substroke HMM. In stroke-based methods, a large number of stroke-order variations can be easily expressed by just adding different stroke sequences to the dictionary and it is not necessary to train new reference patterns. The hierarchical structured dictionary has an advantage that thousands of stroke-order variations of Kanji characters can be produced using a small number of stroke-order rules defining Kanji parts. Moreover, the recognition speed is fast since common sequences are shared in a substroke network, even if the total number of stroke-order combinations becomes enormous practically. In experiments, 300 different stroke-order rules of Kanji parts were statistically chosen by using 60 writers' handwritings of 1,016 educational Kanji characters. By adding these new stroke-order rules to the dictionary, about 9,000 variations of different stroke-orders were generated for 2,965 JIS 1st level Kanji characters. As a result, we successfully improved the recognition accuracy from 82.6% to 90.2% for stroke-order free handwritings.
Speech, Sound and Music Processing: Embracing Research in India, 2012
HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific r... more HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
We propose a new statistical model, named Hierarchical Topic Trajectory Model (HTTM), for acquiri... more We propose a new statistical model, named Hierarchical Topic Trajectory Model (HTTM), for acquiring a dynamically changing topic model that represents the relationship between video frames and associated text labels. Model parameter estimation, annotation and retrieval can be executed within a unified framework with a few computation. It is also easy to add new modals such as audio signal and geotags. Preliminary experiments on video annotation task with manually annotated video dataset indicate that our proposed method can improve the annotation accuracy.
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
This paper presents a new sparse representation for acoustic signals which is based on a mixing m... more This paper presents a new sparse representation for acoustic signals which is based on a mixing model defined in the complex-spectrum domain (where additivity holds), and allows us to extract recurrent patterns of magnitude spectra that underlie observed complex spectra and the phase estimates of constituent signals. An efficient iterative algorithm is derived, which reduces to the multiplicative update algorithm for non-negative matrix factorization developed by Lee under a particular condition.
Wiener filtering is one of the most widely used methods in audio source separation. It is often a... more Wiener filtering is one of the most widely used methods in audio source separation. It is often applied on time-frequency representations of signals, such as the short-time Fourier transform (STFT), to exploit their short-term stationarity, but so far the design of the Wiener time-frequency mask did not take into account the necessity for the output spectrograms to be consistent, i.e., to correspond to the STFT of a time-domain signal. In this paper, we generalize the concept of Wiener filtering to time-frequency masks which can involve manipulation of the phase as well by formulating the problem as a consistency-constrained Maximum-Likelihood one. We present two methods to solve the problem, one looking for the optimal time-domain signal, the other promoting consistency through a penalty function directly in the time-frequency domain. We show through experimental evaluation that, both in oracle conditions and combined with spectral subtraction, our method outperforms classical Wiener filtering.
In this paper, a co-channel multi-pitch detection algorithm is described. We suggest the importan... more In this paper, a co-channel multi-pitch detection algorithm is described. We suggest the importance of this when prosodic information is need to be extracted separately from respective F 0 patterns of concurrent utterances. Though temporal continuity of speech prosody should be considered, we discuss a process done independently on each single frame as the first step. A model of multiple harmonic structures is constructed with a mixture of tied Gaussian mixtures with which a single harmonic structure is modeled. Our algorithm enables to detect both a number of concurrent speakers, and each spectral envelope of underlying harmonic structure based on a maximum likelihood estimation of the model parameters using EM algorithm and an information criterion. It operates without a priori information of F 0 contours and a restriction of a number of speakers, and it also extracts accurate F 0 s as continuous values with simple procedures in spectral domain. Experiments showed our algorithm outperformed well-known cepstrum for both speech signals of a single speaker and simultaneous two speakers.
This paper proposes a Hidden Markov Model (HMM)-based algorithm for automatic decision of piano f... more This paper proposes a Hidden Markov Model (HMM)-based algorithm for automatic decision of piano fingering. We represent the positions and forms of hands and fingers as HMM states and model the resulted sequence of performed notes as emissions associated with HMM transitions. Optimal fingering decision is thus formulated as Viterbi search to find the most likely sequence of state transitions. The proposed algorithm models the required efforts in pressing a key with a finger followed by another key with another finger, and in two-dimensional positioning of fingers on the piano keyboard with diatonic and chromatic keys. Fundamental functionality of the algorithm was verified through experiments with real piano pieces. This framework can be extended to polyphonic music containing chords.
ABSTRACT This extended abstract details submissions to the Music Information Retrieval Evaluation... more ABSTRACT This extended abstract details submissions to the Music Information Retrieval Evaluation eXchange. Three or four feature sets based on musical knowledge [1] were cal-culated. Then an SVM with the ANOVA RBF kernel was used, or a canonical LDA was conducted with effective features chosen from the sets. AIC values or accuracy of cross validation of training data were calculated for the feature selection. Four submissions were made by com-binations of feature sets and classifiers.
2009 IEEE International Conference on Multimedia and Expo, 2009
Many musical genres and styles are characterized by distinct representative rhythmic patterns. In... more Many musical genres and styles are characterized by distinct representative rhythmic patterns. In most automatic genre classification systems global statistical features based on timbral dynamics such as Mel-Frequency Cepstral Coefficients (MFCC) are utilized but so far rhythmic information has not so effectively been used. In order to extract bar-long unit rhythmic patterns for a music collection we propose a clustering method based on one-pass dynamic programming and k-means clustering. After extracting the fundamental rhythmic patterns for each style/genre a pattern occurrence histogram is calculated and used as a feature vector for supervised learning. Experimental results show that the automatically calculated rhythmic pattern information can be used to effectively classify musical genre/style and improve upon current approaches based on timbral features.
Given a relatively small selection of guitar scores for a large population of guitarists, there s... more Given a relatively small selection of guitar scores for a large population of guitarists, there should be a certain demand for systems that can automatically arrange an arbitrary score for guitars. Our aim in this paper is to formulate the "fingering decision" and "arrangement" in a unified framework that can be cast as a decoding problem of a hidden Markov model (HMM). The left hand forms on the fingerboard are considered as the hidden states and the note sequence of a given score as an observed sequence generated by the HMM. Finding the most likely sequence of the hidden states thus corresponds to performing fingering decision or arrangement. The manual setting of HMM parameters reflecting preference of beginner guitarists lets the framework generate natural fingerings and arrangements suitable for beginners. Some examples of fingering and arrangement produced by the proposed method are presented.
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Papers by 茂樹 嵯峨山