Simultaneous Identification and Localization of Still and Mobile Speakers Based on Binaural Robot Audition

2017 ◽  
Vol 29 (1) ◽  
pp. 59-71 ◽  
Author(s):  
Karim Youssef ◽  
◽  
Katsutoshi Itoyama ◽  
Kazuyoshi Yoshii
Keyword(s):  
Neural Networks ◽  
Speaker Identification ◽  
Position Estimation ◽  
Acoustic Features ◽  
Front End ◽  
Robot Audition ◽  
Speaker Tracking ◽  
Common Signal ◽  
Interaural Level Differences ◽  
Simultaneous Identification

[abstFig src='/00290001/06.jpg' width='300' text='Efficient mobile speaker tracking' ] This paper jointly addresses the tasks of speaker identification and localization with binaural signals. The proposed system operates in noisy and echoic environments and involves limited computations. It demonstrates that a simultaneous identification and localization operation can benefit from a common signal processing front end for feature extraction. Moreover, a joint exploitation of the identity and position estimation outputs allows the outputs to limit each other’s errors. Equivalent rectangular bandwidth frequency cepstral coefficients (ERBFCC) and interaural level differences (ILD) are extracted. These acoustic features are respectively used for speaker identity and azimuth estimation through artificial neural networks (ANNs). The system was evaluated in simulated and real environments, with still and mobile speakers. Results demonstrate its ability to produce accurate estimations in the presence of noises and reflections. Moreover, the advantage of the binaural context over the monaural context for speaker identification is shown.

scholarly journals Acoustic feature-based sentiment analysis of call center data

2017 ◽  
Author(s):  
◽  
Zeshan Peng
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Emotion Recognition ◽  
Sentiment Analysis ◽  
Call Center ◽  
Machine Learning Algorithms ◽  
Language Recognition ◽  
Acoustic Features ◽  
Learning Methods ◽  
Machine Learning Methods

With the advancement of machine learning methods, audio sentiment analysis has become an active research area in recent years. For example, business organizations are interested in persuasion tactics from vocal cues and acoustic measures in speech. A typical approach is to find a set of acoustic features from audio data that can indicate or predict a customer's attitude, opinion, or emotion state. For audio signals, acoustic features have been widely used in many machine learning applications, such as music classification, language recognition, emotion recognition, and so on. For emotion recognition, previous work shows that pitch and speech rate features are important features. This thesis work focuses on determining sentiment from call center audio records, each containing a conversation between a sales representative and a customer. The sentiment of an audio record is considered positive if the conversation ended with an appointment being made, and is negative otherwise. In this project, a data processing and machine learning pipeline for this problem has been developed. It consists of three major steps: 1) an audio record is split into segments by speaker turns; 2) acoustic features are extracted from each segment; and 3) classification models are trained on the acoustic features to predict sentiment. Different set of features have been used and different machine learning methods, including classical machine learning algorithms and deep neural networks, have been implemented in the pipeline. In our deep neural network method, the feature vectors of audio segments are stacked in temporal order into a feature matrix, which is fed into deep convolution neural networks as input. Experimental results based on real data shows that acoustic features, such as Mel frequency cepstral coefficients, timbre and Chroma features, are good indicators for sentiment. Temporal information in an audio record can be captured by deep convolutional neural networks for improved prediction accuracy.

scholarly journals Training neural networks to recognize speech increased their correspondence to the human auditory pathway but did not yield a shared hierarchy of acoustic features

2021 ◽  
Author(s):  
Jessica A.F. Thompson ◽  
Yoshua Bengio ◽  
Elia Formisano ◽  
Marc Schönwiesner
Keyword(s):  
Neural Networks ◽  
Recognition Task ◽  
Single Layer ◽  
Auditory Pathway ◽  
7 Tesla ◽  
List Type ◽  
Acoustic Features ◽  
Visual Areas ◽  
Cortical Regions ◽  
Fully Connected

AbstractThe correspondence between the activity of artificial neurons in convolutional neural networks (CNNs) trained to recognize objects in images and neural activity collected throughout the primate visual system has been well documented. Shallower layers of CNNs are typically more similar to early visual areas and deeper layers tend to be more similar to later visual areas, providing evidence for a shared representational hierarchy. This phenomenon has not been thoroughly studied in the auditory domain. Here, we compared the representations of CNNs trained to recognize speech (triphone recognition) to 7-Tesla fMRI activity collected throughout the human auditory pathway, including subcortical and cortical regions, while participants listened to speech. We found no evidence for a shared representational hierarchy of acoustic speech features. Instead, all auditory regions of interest were most similar to a single layer of the CNNs: the first fully-connected layer. This layer sits at the boundary between the relatively task-general intermediate layers and the highly task-specific final layers. This suggests that alternative architectural designs and/or training objectives may be needed to achieve fine-grained layer-wise correspondence with the human auditory pathway.HighlightsTrained CNNs more similar to auditory fMRI activity than untrainedNo evidence of a shared representational hierarchy for acoustic featuresAll ROIs were most similar to the first fully-connected layerCNN performance on speech recognition task positively associated with fmri similarity

scholarly journals Ensemble of neural networks for 3D position estimation in monolithic PET detectors

2019 ◽  
Vol 64 (19) ◽  
pp. 195010 ◽  
Author(s):  
A Iborra ◽  
A J González ◽  
A González-Montoro ◽  
A Bousse ◽  
D Visvikis
Keyword(s):  
Neural Networks ◽  
Position Estimation ◽  
Pet Detectors

scholarly journals Acoustic comparison of electronics disguised voice using Different semitones

2018 ◽  
Vol 7 (2.16) ◽  
pp. 98 ◽  
Author(s):  
Mahesh K. Singh ◽  
A K. Singh ◽  
Narendra Singh
Keyword(s):  
Support Vector Machine ◽  
Acoustic Analysis ◽  
Speaker Identification ◽  
Support Vector ◽  
Acoustic Features ◽  
Acoustic Feature ◽  
Mel Frequency Cepstral Coefficients ◽  
Identification Rate ◽  
Normal Voice ◽  
Feature Based

This paper emphasizes an algorithm that is based on acoustic analysis of electronics disguised voice. Proposed work is given a comparative analysis of all acoustic feature and its statistical coefficients. Acoustic features are computed by Mel-frequency cepstral coefficients (MFCC) method and compare with a normal voice and disguised voice by different semitones. All acoustic features passed through the feature based classifier and detected the identification rate of all type of electronically disguised voice. There are two types of support vector machine (SVM) and decision tree (DT) classifiers are used for speaker identification in terms of classification efficiency of electronically disguised voice by different semitones.  

Sign in / Sign up

Export Citation Format

Share Document