Environmental sound classification using spectral dynamic features

Karbasi, Mahdie; Ahadi, Seyed Mohammad; Bahmanian, Meysam

doi:10.1109/icics.2011.6173513

Cited by 19 publications

(18 citation statements)

References 9 publications

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“…On the on hand, most of the sound-based recognizers are limited in recognizing human speech, together with some of its characteristics like speaker recognition and his emotional state in order to obtain detailed information about their subject of interest. On the other hand, there are very few studies, which aim to examine in an abstract way the daily human activities in a home environment according to their acoustic characteristics [9,10,12,16,18,20,21]. Despite their generalized way of analyzing sounds, they are all developed in a healthcare perspective and often make the implication that certain sound implies certain activity, which is not necessarily true.…”

Section: Sound-based Activity Recognitionmentioning

confidence: 98%

Analyzing Sounds of Home Environment for Device Recognition

Dimitrov¹,

Britz²,

Brandherm³

et al. 2014

Lecture Notes in Computer Science

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Abstract. Home environments are one of the subjects of study regarding ambient intelligent systems for various purposes, including development of assistance systems for the elderly and energy consumption optimization. Sensing the environmental state via different sensors is the first and crucial component of every ambient intelligent system. In this work we investigate the use of environmental sounds for touch-free audio-based device recognition in a home environment. For this purpose, we analyzed sound characteristics of typical home appliances using different processing techniques. We are using the acquired knowledge to develop a flexible set of features, which can be set manually or determined automatically. To classify the device-specific acoustic fingerprintsconsisting of a significant subset of our features -we use established supervised learning techniques, whereby we optimized the straightforward ones. After building a recognition basis for the recognition of fixed length sound buffers on demand, we implemented a live recognition mode for real-time environment monitoring, providing runtime setup adjustments. We then extended our work with the recognition of untrained, simultaneously working, known devices by mixing their records, utilizing semi-supervised learning. We then anticipated promising results in our evaluation in various aspects, including recognition rate, performance for the different combinations of features, as well as to study the reliability of an automatic mixing of trained data.

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Section: Sound-based Activity Recognitionmentioning

confidence: 98%

Analyzing Sounds of Home Environment for Device Recognition

Dimitrov¹,

Britz²,

Brandherm³

et al. 2014

Lecture Notes in Computer Science

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“…, y N ] be a matrix with columns y i of feature vectors for N sub-frames. For each row of Y, the N-point FFT is applied followed by the logarithmic filter bank, and [25] that the combined features of MFCC and MFCC give the performance bound of static features, which is not improved by adding more conventional features. A system with a feature vector consisting of ZCR, Band-Energy, LPC, LPCC, MFCC, and MFCC, performs poorly as compared to that with only MFCC and MFCC under the SVM or GMM classifiers.…”

Section: I I S T a T I O N A R Y E S R T E C H N I Q U E Smentioning

confidence: 99%

“…Another example was recently proposed by Karbasi et al . [25], which attempted to capture the temporal variation among sub-frames in a new set of features called “Spectral Dynamic Features (SDF)” as detailed below.…”

Section: Stationary Esr Techniquesmentioning

confidence: 99%

Environmental sound recognition: a survey

Chachada

Kuo

2014

SIP

114

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Although research in audio recognition has traditionally focused on speech and music signals, the problem of environmental sound recognition (ESR) has received more attention in recent years. Research on ESR has significantly increased in the past decade. Recent work has focused on the appraisal of non-stationary aspects of environmental sounds, and several new features predicated on non-stationary characteristics have been proposed. These features strive to maximize their information content pertaining to signal's temporal and spectral characteristics. Furthermore, sequential learning methods have been used to capture the long-term variation of environmental sounds. In this survey, we will offer a qualitative and elucidatory survey on recent developments. It includes four parts: (i) basic environmental sound-processing schemes, (ii) stationary ESR techniques, (iii) non-stationary ESR techniques, and (iv) performance comparison of selected methods. Finally, concluding remarks and future research and development trends in the ESR field will be given.

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“…One variation to exploit the temporal structure is when a signal-model is learned based on features from all ordered sub-frames such as HMM. Another example was recently proposed by Karbasi et al [14], which attempted to capture the temporal variation among sub-frames in a new set of features called "Spectral Dynamic Features (SDF)" as detailed below.…”

Section: Stationary Esr Techniquesmentioning

confidence: 99%

“…The superior performance of SDF against several conventional features such as ZCR, LPC, MFCC under three classifiers (i.e., KNN, GMM and SVM) was demonstrated. It was shown in [14] that the combined features of MFCC and ∆MFCC give the performance bound of static features, which is not improved by adding more conventional features. A system with a feature vector consisting of ZCR, Band-Energy, LPC, LPCC, MFCC and ∆MFCC, performs poorly as compared to that with only MFCC and ∆MFCC under the SVM or GMM classifiers.…”

Section: Stationary Esr Techniquesmentioning

confidence: 99%

Environmental sound recognition: A survey

Chachada

Kuo

2013

2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference

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Although research in audio recognition has traditionally focused on speech and music signals, the problem of environmental sound recognition (ESR) has received more attention in recent years. Research on ESR has significantly increased in the past decade. Recent work has focused on the appraisal of non-stationary aspects of environmental sounds, and several new features predicated on non-stationary characteristics have been proposed. These features strive to maximize their information content pertaining to signal's temporal and spectral characteristics. Furthermore, sequential learning methods have been used to capture the long-term variation of environmental sounds. In this survey, we will offer a qualitative and elucidatory survey on recent developments. It includes three parts: i) basic environmental sound processing schemes, ii) stationary ESR techniques and iii) non-stationary ESR techniques. Finally, concluding remarks and future research and development trends in the ESR field will be given.

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Environmental sound classification using spectral dynamic features

Cited by 19 publications

References 9 publications

Analyzing Sounds of Home Environment for Device Recognition

Analyzing Sounds of Home Environment for Device Recognition

Environmental sound recognition: a survey

Environmental sound recognition: A survey

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