NDLI: Two-stage supervised learning-based method to detect screams and cries in urban environments

Content Provider	ACM Digital Library
Author	Sharma, Anil Kaul, Sanjit
Copyright Year	2016
Abstract	Smartphones can enable monitoring signs of distress as a human goes about his daily routine. Motivated by this possibility of 24x7 distress detection, we investigate detection of screaming and crying in urban environments, which we categorize into the contexts of indoors (home and office), outdoors, human conversation, large human gatherings, machinery, and audio from multimedia devices. Prior works are often restricted to specific environments or controlled settings. We propose a novel two-stage supervised learning based method, with tunable decision parameters for each stage, to achieve a desired true distress (scream and cry) detection rate (DR) and false alarm rate (FAR). We observe that the choice of the parameters is a function of the signal-to-noise ratio (SNR) of the distress signal, which is the ratio of the power of the distress signal to the power of the context audio. In the absence of SNR information, we show that a simple SNR estimation scheme performs well. Alternately, we show how the decision parameters can be selected based on the context estimated by the method. We show the results of testing the proposal over hundred hours of audio data recorded by the smartphones of ten volunteers as they went about their daily routines. Achieved performance is exemplified by a DR of 93.16% and a FAR of 4.76% at a SNR of 20 dB. The corresponding values for a SNR of 10 dB are 84.13% and 4.77%. Finally, we compare with, which also deals with audio event detection in noisy environments.
Starting Page	290
Ending Page	299
Page Count	10
File Format	PDF
ISSN	23299290
e-ISSN	23299304
DOI	10.1109/TASLP.2015.2506264
Volume Number	24
Issue Number	2
Journal	IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-02-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	MFCC SVM Context estimation Cry detection Scream detection Smartphone Urban environment
Content Type	Text
Resource Type	Article
Subject	Instrumentation Computational Mathematics Signal Processing Electrical and Electronic Engineering Acoustics and Ultrasonics Speech and Hearing Media Technology

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 Library 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

Two-Stage Supervised Learning-Based Method to Detect Screams and Cries in Urban Environments

Joint detection and estimation of speech spectral amplitude using noncontinuous gain functions

Aligned-Parallel-Corpora Based Semi-Supervised Learning for Arabic Mention Detection

On training targets for supervised speech separation

Joint Detection and Estimation of Speech Spectral Amplitude Using Noncontinuous Gain Functions

A pairwise algorithm using the deep stacking network for speech separation and pitch estimation

On Training Targets for Supervised Speech Separation

Multiview supervised dictionary learning in speech emotion recognition

Minimum mean-square error estimation of mel-frequency cepstral features-a theoretically consistent approach

Two-stage supervised learning-based method to detect screams and cries in urban environments

Similar Documents

Two-Stage Supervised Learning-Based Method to Detect Screams and Cries in Urban Environments

Joint detection and estimation of speech spectral amplitude using noncontinuous gain functions

Aligned-Parallel-Corpora Based Semi-Supervised Learning for Arabic Mention Detection

On training targets for supervised speech separation

Joint Detection and Estimation of Speech Spectral Amplitude Using Noncontinuous Gain Functions

A pairwise algorithm using the deep stacking network for speech separation and pitch estimation

On Training Targets for Supervised Speech Separation

Multiview supervised dictionary learning in speech emotion recognition

Minimum mean-square error estimation of mel-frequency cepstral features-a theoretically consistent approach

Two-stage supervised learning-based method to detect screams and cries in urban environments