SoundButton: design of a low power wearable audio classification system

Stäger, Mathias; Lukowicz, Paul; Perera, N.; Büren, T. von; Tröster, Gerhard; Starner﻿﻿, Thad

doi:10.1109/iswc.2003.1241387

Cited by 31 publications

(22 citation statements)

References 13 publications

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“…Similar work [18] used FFT parameters of f s =4.8kHz and t w =50 ms (256 points), for this experiment t w was increased to 100 ms. With these parameters LDA classification was applied to successive t w frames within each of the class partitioned samples -returning a hard classification for each frame. Judging accuracy by the number of correctly matching frames over the total number of frames in each sample, an overall recognition rate of 90.18% was obtained.…”

Section: Resultsmentioning

confidence: 99%

“…tools, appliances, or parts of the machinery) [10]). It presents a novel way of combining motion (acceleration) sensor based gesture recognition [8] with sound data from distributed microphones [18]. In particular we exploit intensity differences between a microphone on the wrist of the dominant hand and on the chest to identify relevant actions performed by the user's hand.…”

Section: Paper Aims and Contributionsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Paper Aims and Contributionsmentioning

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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show abstract

“…The acoustic event recognition for four different environments -kitchen, workshop (maintenance), office and outdoors -has been applied in [SLP+03]. The paper discusses a prototype of a sound recognition system focused on an ultra low power hardware implementation in a button-like miniature form.…”

Section: Audio Recognition For a Given Environmentmentioning

confidence: 99%

Acoustic Event Detection and Classification

Temko

Nadeu

Macho

et al. 2009

Computers in the Human Interaction Loop

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The human activity that takes place in meeting-rooms or class-rooms is reflected in a rich variety of acoustic events, either produced by the human body or by objects handled by humans, so the determination of both the identity of sounds and their position in time may help to detect and describe that human activity. Additionally, detection of sounds other than speech may be useful to enhance the robustness of speech technologies like automatic speech recognition.Automatic detection and classification of acoustic events is the objective of this thesis work. It aims at processing the acoustic signals collected by distant microphones in meeting-room or classroom environments to convert them into symbolic descriptions corresponding to a listener's perception of the different sound events that are present in the signals and their sources.First of all, the task of acoustic event classification is faced using Support Vector Machine (SVM) classifiers, which are motivated by the scarcity of training data. A confusion-matrix-based variable-feature-set clustering scheme is developed for the multiclass recognition problem, and tested on the gathered database. With it, a higher classification rate than the GMM-based technique is obtained, arriving to a large relative average error reduction with respect to the best result from the conventional binary tree scheme. Moreover, several ways to extend SVMs to sequence processing are compared, in an attempt to avoid the drawback of SVMs when dealing with audio data, i.e. their restriction to work with fixed-length vectors, observing that the dynamic time warping kernels work well for sounds that show a temporal structure. Furthermore, concepts and tools from the fuzzy theory are used to investigate, first, the importance of and degree of interaction among features, and second, ways to fuse the outputs of several classification systems. The developed AEC systems are Two kinds of databases are used: two databases of isolated acoustic events, and a database of interactive seminars containing a significant number of acoustic events of interest. Our developed systems, which consist of SVM-based classification within a sliding window plus post-processing, were the only submissions not using HMMs, and each of them obtained competitive results in the corresponding evaluation.vi Speech activity detection was also pursued in this thesis since, in fact, it is a -especially important -particular case of acoustic event detection. An enhanced SVM training approach for the speech activity detection task is developed, mainly to cope with the problem of dataset reduction.The resulting SVM-based system is tested with several NIST Rich Transcription (RT) evaluation datasets, and it shows better scores than our GMM-based system, which ranked among the best systems in the RT06 evaluation.Finally, it is worth mentioning a few side outcomes from this thesis work. As it has been carried out in the framework of the CHIL EU project, the author has been responsible for the organization of the above m...

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“…These 256 point windows are sampled at 1 second intervals. This window size is suggested and experimentally validated in [16]. To give each dimension equal importance in terms of the self-organization process, we normalized the data to unit coordinate-wise variance as proposed in [9].…”

Section: Preprocessingmentioning

confidence: 99%

“…In our experiments, the top five eigenspaces explained most of the sensor variance, so the input data was projected onto the first five principal components. For the audio samples, we calculate a predefined linear transformation averaging over 16 adjacent frequency components to obtain an eight channel subband decomposition, which is normalized to unit-length in order to ignore intensity which is taken into account separately, as suggested in [16]. Table 1 summarizes the various preprocessing steps done for the different sensors.…”

Section: Preprocessingmentioning

confidence: 99%

Context-aware mobile computing: learning context- dependent personal preferences from a wearable sensor array

Krause

Smailagic

Siewiorek

2006

IEEE Trans. on Mobile Comput.

176

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Abstract-Context-aware computing describes the situation where a wearable/mobile computer is aware of its user's state and surroundings and modifies its behavior based on this information. We designed, implemented, and evaluated a wearable system which can learn context-dependent personal preferences by identifying individual user states and observing how the user interacts with the system in these states. This learning occurs online and does not require external supervision. The system relies on techniques from machine learning and statistical analysis. A case study integrates the approach in a context-aware mobile phone. The results indicate that the method is able to create a meaningful user context model while only requiring data from comfortable wearable sensor devices.

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SoundButton: design of a low power wearable audio classification system

Abstract: Abstract

Cited by 31 publications

References 13 publications

Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers

Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers

Acoustic Event Detection and Classification

Context-aware mobile computing: learning context- dependent personal preferences from a wearable sensor array

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