Computational auditory scene recognition

Peltonen,; Tuomi,; Klapuri,; Huopaniemi,; Sorsa,

doi:10.1109/icassp.2002.1006149

Cited by 73 publications

(56 citation statements)

References 2 publications

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“…The reported work continues the research first described in [1]. In this paper, we describe a listening test made to facilitate the direct comparison of the system's performance to that of human subjects.…”

Section: Introductionmentioning

confidence: 87%

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Audio-based context awareness acoustic modeling and perceptual evaluation

Eronen¹,

Tuomi²,

Klapuri³

et al. 2003

2003 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (IEEE Cat. No.03TH8684)

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This paper concerns the development of a system for the recognition of a context or an environment based on acoustic information only. Our system uses mel-frequency cepstral coefficients and their derivatives as features, and continuous density hidden Markov models (HMM) as acoustic models. We evaluate different model topologies and training methods for HMMs and show that discriminative training can yield a 10% reduction in error rate compared to maximum-likelihood training. A listening test is made to study the human accuracy in the task and to obtain a baseline for the assessment of the performance of the system. Direct comparison to human performance indicates that the system performs somewhat worse than human subjects do in the recognition of 18 everyday contexts and almost comparably in recognizing six higher level categories.

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Section: Introductionmentioning

confidence: 87%

“…The recording procedure has been described in [1]. Two training and testing setups were formed from the samples.…”

Section: Acoustic Measurementsmentioning

confidence: 99%

Audio-based context awareness acoustic modeling and perceptual evaluation

Eronen¹,

Tuomi²,

Klapuri³

et al. 2003

2003 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (IEEE Cat. No.03TH8684)

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“…However all of the above work focused on recognizing comparatively simple activities (walking, running, and sitting). Sound based situation analysis has been investigated by Pelton et al and in the wearables domain by Clarkson and Pentland [12,5]. Intelligent hearing aids have also exploited sound analysis to improve their performance [3].…”

Section: Related Workmentioning

confidence: 99%

Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers

Lukowicz

Ward

Junker

et al. 2004

Lecture Notes in Computer Science

207

127

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Abstract. The paper presents a technique to automatically track the progress of maintenance or assembly tasks using body worn sensors. The technique is based on a novel way of combining data from accelerometers with simple frequency matching sound classification. This includes the intensity analysis of signals from microphones at different body locations to correlate environmental sounds with user activity. To evaluate our method we apply it to activities in a wood shop. On a simulated assembly task our system can successfully segment and identify most shop activities in a continuous data stream with zero false positives and 84.4% accuracy.

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“…The second set of features consists of features which have been used by other groups for specific, complex recognition problems, in particular, speech recognition [9,10,13]. In the time domain the following 3 features were calculated: zero crossing rate, fluctuation of amplitude and 90%-10% width of amplitude histogram distribution.…”

Section: Sound Classificationmentioning

confidence: 99%

“…The Audio Research Group at Tampere University, Finland, works on auditory scene recognition, which focuses on recognizing the context or environment, instead of analyzing discrete sound events [13]. In addition, the classification of sound types has been investigated in the context of hearing aid improvements [2].…”

Section: Related Workmentioning

confidence: 99%