Pub Date : 2019-05-12DOI: 10.1109/ICASSP.2019.8683497
S. Inoue, H. Kameoka, Li Li, Shogo Seki, S. Makino
In this paper, we deal with a multichannel source separation problem under a highly reverberant condition. The multichan- nel variational autoencoder (MVAE) is a recently proposed source separation method that employs the decoder distribu- tion of a conditional VAE (CVAE) as the generative model for the complex spectrograms of the underlying source sig- nals. Although MVAE is notable in that it can significantly improve the source separation performance compared with conventional methods, its capability to separate highly rever- berant mixtures is still limited since MVAE uses an instan- taneous mixture model. To overcome this limitation, in this paper we propose extending MVAE to simultaneously solve source separation and dereverberation problems by formulat- ing the separation system as a frequency-domain convolutive mixture model. A convergence-guaranteed algorithm based on the coordinate descent method is derived for the optimiza- tion. Experimental results revealed that the proposed method outperformed the conventional methods in terms of all the source separation criteria in highly reverberant environments.
{"title":"Joint Separation and Dereverberation of Reverberant Mixtures with Multichannel Variational Autoencoder","authors":"S. Inoue, H. Kameoka, Li Li, Shogo Seki, S. Makino","doi":"10.1109/ICASSP.2019.8683497","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8683497","url":null,"abstract":"In this paper, we deal with a multichannel source separation problem under a highly reverberant condition. The multichan- nel variational autoencoder (MVAE) is a recently proposed source separation method that employs the decoder distribu- tion of a conditional VAE (CVAE) as the generative model for the complex spectrograms of the underlying source sig- nals. Although MVAE is notable in that it can significantly improve the source separation performance compared with conventional methods, its capability to separate highly rever- berant mixtures is still limited since MVAE uses an instan- taneous mixture model. To overcome this limitation, in this paper we propose extending MVAE to simultaneously solve source separation and dereverberation problems by formulat- ing the separation system as a frequency-domain convolutive mixture model. A convergence-guaranteed algorithm based on the coordinate descent method is derived for the optimiza- tion. Experimental results revealed that the proposed method outperformed the conventional methods in terms of all the source separation criteria in highly reverberant environments.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"16 1","pages":"96-100"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84952009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-12DOI: 10.1109/ICASSP.2019.8682245
Xiong Xiao, Zhuo Chen, Takuya Yoshioka, Hakan Erdogan, Changliang Liu, D. Dimitriadis, J. Droppo, Y. Gong
Neural network-based speech separation has received a surge of interest in recent years. Previously proposed methods either are speaker independent or extract a target speaker’s voice by using his or her voice snippet. In applications such as home devices or office meeting transcriptions, a possible speaker list is available, which can be leveraged for speech separation. This paper proposes a novel speech extraction method that utilizes an inventory of voice snippets of possible interfering speakers, or speaker enrollment data, in addition to that of the target speaker. Furthermore, an attention-based network architecture is proposed to form time-varying masks for both the target and other speakers during the separation process. This architecture does not reduce the enrollment audio of each speaker into a single vector, thereby allowing each short time frame of the input mixture signal to be aligned and accurately compared with the enrollment signals. We evaluate the proposed system on a speaker extraction task derived from the Libri corpus and show the effectiveness of the method.
{"title":"Single-channel Speech Extraction Using Speaker Inventory and Attention Network","authors":"Xiong Xiao, Zhuo Chen, Takuya Yoshioka, Hakan Erdogan, Changliang Liu, D. Dimitriadis, J. Droppo, Y. Gong","doi":"10.1109/ICASSP.2019.8682245","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8682245","url":null,"abstract":"Neural network-based speech separation has received a surge of interest in recent years. Previously proposed methods either are speaker independent or extract a target speaker’s voice by using his or her voice snippet. In applications such as home devices or office meeting transcriptions, a possible speaker list is available, which can be leveraged for speech separation. This paper proposes a novel speech extraction method that utilizes an inventory of voice snippets of possible interfering speakers, or speaker enrollment data, in addition to that of the target speaker. Furthermore, an attention-based network architecture is proposed to form time-varying masks for both the target and other speakers during the separation process. This architecture does not reduce the enrollment audio of each speaker into a single vector, thereby allowing each short time frame of the input mixture signal to be aligned and accurately compared with the enrollment signals. We evaluate the proposed system on a speaker extraction task derived from the Libri corpus and show the effectiveness of the method.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"124 1","pages":"86-90"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83544170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-12DOI: 10.1109/ICASSP.2019.8682832
Z. Wang, Xi Xiao, Guangwu Hu, Yao Yao, Dianyan Zhang, Zhendong Peng, Qing Li, Shutao Xia
Reinforcement learning is a framework to make sequential decisions. The combination with deep neural networks further improves the ability of this framework. Convolutional nerual networks make it possible to make sequential decisions based on raw pixels information directly and make reinforcement learning achieve satisfying performances in series of tasks. However, convolutional neural networks still have own limitations in representing geometric patterns and long-term dependencies that occur consistently in state inputs. To tackle with the limitation, we propose the self-attention architecture to augment the original network. It provides a better balance between ability to model long-range dependencies and computational efficiency. Experiments on Atari games illustrate that self-attention structure is significantly effective for function approximation in deep reinforcement learning.
{"title":"Non-local Self-attention Structure for Function Approximation in Deep Reinforcement Learning","authors":"Z. Wang, Xi Xiao, Guangwu Hu, Yao Yao, Dianyan Zhang, Zhendong Peng, Qing Li, Shutao Xia","doi":"10.1109/ICASSP.2019.8682832","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8682832","url":null,"abstract":"Reinforcement learning is a framework to make sequential decisions. The combination with deep neural networks further improves the ability of this framework. Convolutional nerual networks make it possible to make sequential decisions based on raw pixels information directly and make reinforcement learning achieve satisfying performances in series of tasks. However, convolutional neural networks still have own limitations in representing geometric patterns and long-term dependencies that occur consistently in state inputs. To tackle with the limitation, we propose the self-attention architecture to augment the original network. It provides a better balance between ability to model long-range dependencies and computational efficiency. Experiments on Atari games illustrate that self-attention structure is significantly effective for function approximation in deep reinforcement learning.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"146 1","pages":"3042-3046"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86091462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-12DOI: 10.1109/ICASSP.2019.8683767
R. Luke, D. McAlpine
A novel approach to multi-microphone acoustic source localisation based on spiking neural networks is presented. We demonstrate that a two microphone system connected to a spiking neural network can be used to localise acoustic sources based purely on inter microphone timing differences, with no need for manually configured delay lines. A two sensor example is provided which includes 1) a front end which converts the acoustic signal to a series of spikes, 2) a hidden layer of spiking neurons, 3) an output layer of spiking neurons which represents the location of the acoustic source. We present details on training the network, and evaluation of its performance in quiet and noisy conditions. The system is trained on two locations, and we show that the lateralisation accuracy is 100% when presented with previously unseen data in quiet conditions. We also demonstrate the network generalises to modulation rates and background noise on which it was not trained.
{"title":"A Spiking Neural Network Approach to Auditory Source Lateralisation","authors":"R. Luke, D. McAlpine","doi":"10.1109/ICASSP.2019.8683767","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8683767","url":null,"abstract":"A novel approach to multi-microphone acoustic source localisation based on spiking neural networks is presented. We demonstrate that a two microphone system connected to a spiking neural network can be used to localise acoustic sources based purely on inter microphone timing differences, with no need for manually configured delay lines. A two sensor example is provided which includes 1) a front end which converts the acoustic signal to a series of spikes, 2) a hidden layer of spiking neurons, 3) an output layer of spiking neurons which represents the location of the acoustic source. We present details on training the network, and evaluation of its performance in quiet and noisy conditions. The system is trained on two locations, and we show that the lateralisation accuracy is 100% when presented with previously unseen data in quiet conditions. We also demonstrate the network generalises to modulation rates and background noise on which it was not trained.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"5 1","pages":"1488-1492"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86447868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-12DOI: 10.1109/ICASSP.2019.8683552
Henning Lange, M. Berges, J. Z. Kolter
In this paper, an algorithm for performing System Identification and inference of the filtering recursion for stochastic non-linear dynamical systems is introduced. Additionally, the algorithm allows for enforcing domain-constraints of the state variable. The algorithm makes use of an approximate inference technique called Variational Inference in conjunction with Deep Neural Networks as the optimization engine. Although general in its nature, the algorithm is evaluated in the context of Non-Intrusive Load Monitoring, the problem of inferring the operational state of individual electrical appliances given aggregate measurements of electrical power collected in a home.
{"title":"Neural Variational Identification and Filtering for Stochastic Non-linear Dynamical Systems with Application to Non-intrusive Load Monitoring","authors":"Henning Lange, M. Berges, J. Z. Kolter","doi":"10.1109/ICASSP.2019.8683552","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8683552","url":null,"abstract":"In this paper, an algorithm for performing System Identification and inference of the filtering recursion for stochastic non-linear dynamical systems is introduced. Additionally, the algorithm allows for enforcing domain-constraints of the state variable. The algorithm makes use of an approximate inference technique called Variational Inference in conjunction with Deep Neural Networks as the optimization engine. Although general in its nature, the algorithm is evaluated in the context of Non-Intrusive Load Monitoring, the problem of inferring the operational state of individual electrical appliances given aggregate measurements of electrical power collected in a home.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"8 1","pages":"8340-8344"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82402365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-12DOI: 10.1109/ICASSP.2019.8682568
Qiuwei Li, Zhihui Zhu, Gongguo Tang, M. Wakin
A variety of unconstrained nonconvex optimization problems have been shown to have benign geometric landscapes that satisfy the strict saddle property and have no spurious local minima. We present a general result relating the geometry of an unconstrained centralized problem to its equality-constrained distributed extension. It follows that many global consensus problems inherit the benign geometry of their original centralized counterpart. Taking advantage of this fact, we demonstrate the favorable performance of the Gradient ADMM algorithm on a distributed low-rank matrix approximation problem.
{"title":"The Geometry of Equality-constrained Global Consensus Problems","authors":"Qiuwei Li, Zhihui Zhu, Gongguo Tang, M. Wakin","doi":"10.1109/ICASSP.2019.8682568","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8682568","url":null,"abstract":"A variety of unconstrained nonconvex optimization problems have been shown to have benign geometric landscapes that satisfy the strict saddle property and have no spurious local minima. We present a general result relating the geometry of an unconstrained centralized problem to its equality-constrained distributed extension. It follows that many global consensus problems inherit the benign geometry of their original centralized counterpart. Taking advantage of this fact, we demonstrate the favorable performance of the Gradient ADMM algorithm on a distributed low-rank matrix approximation problem.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"19 1","pages":"7928-7932"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78630688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-12DOI: 10.1109/ICASSP.2019.8683742
Gen Li, Jingkai Yan, Yuantao Gu
Compressed sensing seeks to recover an unknown sparse vector from undersampled rate measurements. Since its introduction, there have been enormous works on compressed sensing that develop efficient algorithms for sparse signal recovery. The restricted isometry property (RIP) has become the dominant tool used for the analysis of exact reconstruction from seemingly undersampled measurements. Although the upper bound of the RIP constant has been studied extensively, as far as we know, the result is missing for the lower bound. In this work, we first present a tight lower bound for the RIP constant, filling the gap there. The lower bound is at the same order as the upper bound for the RIP constant. Moreover, we also show that our lower bound is close to the upper bound by numerical simulations. Our bound on the RIP constant provides an information-theoretic lower bound about the sampling rate for the first time, which is the essential question for practitioners.
{"title":"Information Theoretic Lower Bound of Restricted Isometry Property Constant","authors":"Gen Li, Jingkai Yan, Yuantao Gu","doi":"10.1109/ICASSP.2019.8683742","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8683742","url":null,"abstract":"Compressed sensing seeks to recover an unknown sparse vector from undersampled rate measurements. Since its introduction, there have been enormous works on compressed sensing that develop efficient algorithms for sparse signal recovery. The restricted isometry property (RIP) has become the dominant tool used for the analysis of exact reconstruction from seemingly undersampled measurements. Although the upper bound of the RIP constant has been studied extensively, as far as we know, the result is missing for the lower bound. In this work, we first present a tight lower bound for the RIP constant, filling the gap there. The lower bound is at the same order as the upper bound for the RIP constant. Moreover, we also show that our lower bound is close to the upper bound by numerical simulations. Our bound on the RIP constant provides an information-theoretic lower bound about the sampling rate for the first time, which is the essential question for practitioners.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"25 1","pages":"5297-5301"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84616757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-12DOI: 10.1109/ICASSP.2019.8683801
Lukas Stappen, N. Cummins, Eva-Maria Messner, H. Baumeister, J. Dineley, Björn Schuller
Automatic detection of sentiment and affect in personal narratives through word usage has the potential to assist in the automated detection of change in psychotherapy. Such a tool could, for instance, provide an efficient, objective measure of the time a person has been in a positive or negative state-of-mind. Towards this goal, we propose and develop a hierarchical attention model for the tasks of sentiment (positive and negative) and self-assessed affect detection in transcripts of personal narratives. We also perform a qualitative analysis of the word attentions learnt by our sentiment analysis model. In a key result, our attention model achieved an un-weighted average recall (UAR) of 91.0 % in a binary sentiment detection task on the test partition of the Ulm State-of-Mind in Speech (USoMS) corpus. We also achieved UARs of 73.7 % and 68.6 % in the 3-class tasks of arousal and valence detection respectively. Finally, our qualitative analysis associates colloquial reinforcements with positive sentiments, and uncertain phrasing with negative sentiments.
{"title":"Context Modelling Using Hierarchical Attention Networks for Sentiment and Self-assessed Emotion Detection in Spoken Narratives","authors":"Lukas Stappen, N. Cummins, Eva-Maria Messner, H. Baumeister, J. Dineley, Björn Schuller","doi":"10.1109/ICASSP.2019.8683801","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8683801","url":null,"abstract":"Automatic detection of sentiment and affect in personal narratives through word usage has the potential to assist in the automated detection of change in psychotherapy. Such a tool could, for instance, provide an efficient, objective measure of the time a person has been in a positive or negative state-of-mind. Towards this goal, we propose and develop a hierarchical attention model for the tasks of sentiment (positive and negative) and self-assessed affect detection in transcripts of personal narratives. We also perform a qualitative analysis of the word attentions learnt by our sentiment analysis model. In a key result, our attention model achieved an un-weighted average recall (UAR) of 91.0 % in a binary sentiment detection task on the test partition of the Ulm State-of-Mind in Speech (USoMS) corpus. We also achieved UARs of 73.7 % and 68.6 % in the 3-class tasks of arousal and valence detection respectively. Finally, our qualitative analysis associates colloquial reinforcements with positive sentiments, and uncertain phrasing with negative sentiments.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"47 14","pages":"6680-6684"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91435895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-12DOI: 10.1109/ICASSP.2019.8683388
Jisung Wang, Sangki Kim, Yeha Lee
Data augmentation is crucial to improving the performance of deep neural networks by helping the model avoid overfitting and improve its generalization. In automatic speech recognition, previous work proposed several approaches to augment data by performing speed perturbation or spectral transformation. Since data augmented in this manner has similar acoustic representations as the original data, it has limited advantage in improving generalization of the acoustic model. In order to avoid generating data with limited diversity, we propose a voice conversion approach using a generative model (WaveNet), which generates a new utterance by transforming an utterance to a given target voice. Our method synthesizes speech with diverse pitch patterns by minimizing the use of acoustic features. With the Wall Street Journal dataset, we verify that our method led to better generalization compared to other data augmentation techniques such as speed perturbation and WORLD-based voice conversion. In addition, when combined with the speed perturbation technique, the two methods complement each other to further improve performance of the acoustic model.
{"title":"Speech Augmentation Using Wavenet in Speech Recognition","authors":"Jisung Wang, Sangki Kim, Yeha Lee","doi":"10.1109/ICASSP.2019.8683388","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8683388","url":null,"abstract":"Data augmentation is crucial to improving the performance of deep neural networks by helping the model avoid overfitting and improve its generalization. In automatic speech recognition, previous work proposed several approaches to augment data by performing speed perturbation or spectral transformation. Since data augmented in this manner has similar acoustic representations as the original data, it has limited advantage in improving generalization of the acoustic model. In order to avoid generating data with limited diversity, we propose a voice conversion approach using a generative model (WaveNet), which generates a new utterance by transforming an utterance to a given target voice. Our method synthesizes speech with diverse pitch patterns by minimizing the use of acoustic features. With the Wall Street Journal dataset, we verify that our method led to better generalization compared to other data augmentation techniques such as speed perturbation and WORLD-based voice conversion. In addition, when combined with the speed perturbation technique, the two methods complement each other to further improve performance of the acoustic model.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"24 1","pages":"6770-6774"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80783918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-12DOI: 10.1109/ICASSP.2019.8682661
Song Li, Roman Schlieper, J. Peissig
Reverberation time is an important room acoustical parameter that can be used to identify the acoustic environment, predict speech intelligibility and model the late reverberation for binaural rendering, etc. Several blind estimation algorithms of reverberation time have been proposed by analyzing recorded speech signals. Unfortunately, the estimation accuracy for the frequency dependent reverberation time is lower than for the full-band reverberation time due to the lower signal energy in sub-band filters. This study presents a novel approach for the blind estimation of reverberation time in the full frequency range. The maximum likelihood method is applied for the estimation of the reverberation time from low- to mid-frequencies, and the reverberation time from mid- to high-frequencies is predicted by our proposed model based on the analysis of the reverberation time calculated from room impulse responses in different rooms. The proposed method is validated by two experiments and shows a good performance.
{"title":"A Hybrid Method for Blind Estimation of Frequency Dependent Reverberation Time Using Speech Signals","authors":"Song Li, Roman Schlieper, J. Peissig","doi":"10.1109/ICASSP.2019.8682661","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8682661","url":null,"abstract":"Reverberation time is an important room acoustical parameter that can be used to identify the acoustic environment, predict speech intelligibility and model the late reverberation for binaural rendering, etc. Several blind estimation algorithms of reverberation time have been proposed by analyzing recorded speech signals. Unfortunately, the estimation accuracy for the frequency dependent reverberation time is lower than for the full-band reverberation time due to the lower signal energy in sub-band filters. This study presents a novel approach for the blind estimation of reverberation time in the full frequency range. The maximum likelihood method is applied for the estimation of the reverberation time from low- to mid-frequencies, and the reverberation time from mid- to high-frequencies is predicted by our proposed model based on the analysis of the reverberation time calculated from room impulse responses in different rooms. The proposed method is validated by two experiments and shows a good performance.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"38 1","pages":"211-215"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78067283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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