NDLI: Multi-scale feature based salient environmental sound recognition for machine awareness

Content Provider	IEEE Xplore Digital Library
Author	Jingyu Wang Ke Zhang Madani, K. Sabourin, C.
Copyright Year	2014
Description	Author affiliation: Signals Images & Intell. Syst. Lab., Univ. Paris-Est Creteil Paris, Creteil, France (Madani, K.; Sabourin, C.) \|\| Sch. of Astronaut., Northwestern Polytech. Univ., Xi'an, China (Jingyu Wang; Ke Zhang)
Abstract	Auditory perception of surrounding environment is important to machine awareness. To provide artificial awareness ability for machines, a bio-inspired salient environmental sound detection and recognition method is proposed. The salient sounds are detected by using the auditory saliency map which based on heterogeneous saliency features from visual and acoustic domain. Spectral and temporal saliency features from both power spectral density (PSD) and mel-frequency cepstral coefficients (MFCC) as well as the visual saliency from log-scale spectrogram are applied to yield the final auditory saliency for salient sound detection. To improve the detection accuracy, short-term Shannon entropy (SSE) and a computational inhibition of return (IOR) model are initially proposed to verify the temporal saliency characteristic. The detected salient sounds are classified by using the features which based on the fuzzy vector of spectral energy distribution and MFCC. A two-level classification is presented based on the support vector machine (SVM) for recognition task. Experiments are carried out on the real environmental sound examples. The results show that, over 83% recognition accuracy can be achieved by using proposed fuzzy vector based features, and the overall accuracy of 94.65% can be obtained when combined with MFCC based features.
Starting Page	1
Ending Page	6
File Size	266045
Page Count	6
File Format	PDF
ISBN	9781479973736
DOI	10.1109/ICAwST.2014.6981837
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-29
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Environment sound signal Accuracy Support vector machine classification Artificial awareness Feature extraction Saliency feature fusion SVM Heterogeneous information Fuzzy vector Mel frequency cepstral coefficient MFCC Spectrogram
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Heterogeneous information saliency features' fusion approach for machine's environment sounds based awareness

Insect Sound Recognition Based on MFCC and PNN

A visualized acoustic saliency feature extraction method for environment sound signal processing

Automatic classification of frogs calls based on fusion of features and SVM

Respiratory sound classification using cepstral features and support vector machine

Indian language identification using k-means clustering and support vector machine (SVM)

Characterization between child and adult voice using machine learning algorithm

DSP based improved Speech Recognition system

Gender identification and performance analysis of speech signals

Multi-scale feature based salient environmental sound recognition for machine awareness

Similar Documents

Heterogeneous information saliency features' fusion approach for machine's environment sounds based awareness

Insect Sound Recognition Based on MFCC and PNN

A visualized acoustic saliency feature extraction method for environment sound signal processing

Automatic classification of frogs calls based on fusion of features and SVM

Respiratory sound classification using cepstral features and support vector machine

Indian language identification using k-means clustering and support vector machine (SVM)

Characterization between child and adult voice using machine learning algorithm

DSP based improved Speech Recognition system

Gender identification and performance analysis of speech signals

Multi-scale feature based salient environmental sound recognition for machine awareness