Robust unsupervised detection of human screams in noisy acoustic environments

Nandwana, Mahesh Kumar; Ziaei, Ali; Hansen, John H. L.

doi:10.1109/icassp.2015.7177952

Cited by 30 publications

(23 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The non-speech vocalizations include scream events, and neutral speech for comparison. The experiments on UTNonSpeech-I corpus were conducted and published in earlier studies, where details of the corpus can be found in (Nandwana and Hansen, 2014;Nandwana et al, 2015).…”

Section: A Ut-nonspeech-imentioning

confidence: 99%

“…However, less focus has been seen on the impact of intrinsic sources of mismatch (including scream). Recently, several examples of forensic cases have surfaced in the public eye which motivates research on this topic (Huffington Post, 2013;Nandwana and Hansen, 2014;Nandwana et al, 2015). Such cases exemplify forensic scenarios in which the evidence at hand is recorded under highly intense and stressful conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Few studies have considered scream signal processing and classification (Huang et al, 2010;Liao and Lin, 2009;Mak and Kung, 2012;Nandwana et al, 2015). In Huang et al (2010), a method for real-time scream detection using support vector machines (SVM) was prescribed for homemonitoring applications.…”

Section: Introductionmentioning

confidence: 99%

“…In our earlier study (Nandwana et al, 2015), we proposed an unsupervised approach for detection of human screams from continuous recordings. The proposed solution combines unsupervised vocal activity detection system and T 2 -statistics.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Analysis of human scream and its impact on text-independent speaker verification

Hansen

Nandwana

Shokouhi

2017

The Journal of the Acoustical Society of America

Self Cite

View full text Add to dashboard Cite

Scream is defined as sustained, high-energy vocalizations that lack phonological structure. Lack of phonological structure is how scream is identified from other forms of loud vocalization, such as “yell.” This study investigates the acoustic aspects of screams and addresses those that are known to prevent standard speaker identification systems from recognizing the identity of screaming speakers. It is well established that speaker variability due to changes in vocal effort and Lombard effect contribute to degraded performance in automatic speech systems (i.e., speech recognition, speaker identification, diarization, etc.). However, previous research in the general area of speaker variability has concentrated on human speech production, whereas less is known about non-speech vocalizations. The UT-NonSpeech corpus is developed here to investigate speaker verification from scream samples. This study considers a detailed analysis in terms of fundamental frequency, spectral peak shift, frame energy distribution, and spectral tilt. It is shown that traditional speaker recognition based on the Gaussian mixture models-universal background model framework is unreliable when evaluated with screams.

show abstract

Section: A Ut-nonspeech-imentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of human scream and its impact on text-independent speaker verification

Hansen

Nandwana

Shokouhi

2017

The Journal of the Acoustical Society of America

Self Cite

View full text Add to dashboard Cite

show abstract

“…Much attention has recently been on anomalous sound detection (ASD) such as audio surveillance [1], [2], [3], [4] and equipment inspection [5], [6]. The goal with ASD is to prevent accidents and/or mechanical failures by detecting sounds that do not normally occur, i.e., anomalous sound [7].…”

Section: Introductionmentioning

confidence: 99%

Optimizing acoustic feature extractor for anomalous sound detection based on Neyman-Pearson lemma

Koizumi

Saito

Uematsu

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

Abstract-We propose a method for optimizing an acoustic feature extractor for anomalous sound detection (ASD). Most ASD systems adopt outlier-detection techniques because it is difficult to collect a massive amount of anomalous sound data. To improve the performance of such outlier-detection-based ASD, it is essential to extract a set of efficient acoustic features that is suitable for identifying anomalous sounds. However, the ideal property of a set of acoustic features that maximizes ASD performance has not been clarified. By considering outlierdetection-based ASD as a statistical hypothesis test, we defined optimality as an objective function that adopts Neyman-Pearson lemma; the acoustic feature extractor is optimized to extract a set of acoustic features which maximize the true positive rate under an arbitrary false positive rate. The variational auto-encoder is applied as an acoustic feature extractor and optimized to maximize the objective function. We confirmed that the proposed method improved the F-measure score from 0.02 to 0.06 points compared to those of conventional methods, and ASD results of a stereolithography 3D-printer in a real-environment show that the proposed method is effective in identifying anomalous sounds.

show abstract

Language and Text-Independent Speaker Recognition System Using Energy Spectrum and MFCCs

Doungpaisan

Mingkhwan

2017

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Robust unsupervised detection of human screams in noisy acoustic environments

Cited by 30 publications

References 17 publications

Analysis of human scream and its impact on text-independent speaker verification

Analysis of human scream and its impact on text-independent speaker verification

Optimizing acoustic feature extractor for anomalous sound detection based on Neyman-Pearson lemma

Language and Text-Independent Speaker Recognition System Using Energy Spectrum and MFCCs

Contact Info

Product

Resources

About