Audio Feature and Classifier Analysis for Efficient Recognition of Environmental Sounds

Okuyucu, Cigdem; Sert, Mustafa; Yazıcı, Adnan

doi:10.1109/ism.2013.29

Cited by 18 publications

(9 citation statements)

References 15 publications

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“…rain, engine). For instance, Okuyucu et al 1 present an automatic recognition framework for environmental sounds by using eleven (11) 4 proposed an architecture for sound context recognition, which uses web-collected audio and its crowd-sourced textual descriptions. This is based on Mahalanobis distance and Gaussian Mixture Model (GMM) as a classifier.…”

Section: Related Workmentioning

confidence: 99%

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Feature Selection for Place Classification through Environmental Sounds

Delgado-Contreras

García-Vázquez

Brena

et al. 2014

Procedia Computer Science

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In this work, an environmental audio classification scheme is proposed using a Chi squared filter as a feature selection strategy. Using feature selection (FS), the original 62 features characteristic vector can be optimized, and it can be used for environmental sound classification. These features are obtained using statistical analysis and frequency domain analysis. As a result, we obtain a reduced feature vector composed of 15 features: 11 statistical and 4 of the frequency domain. Using this reduced vector, a 10 class classification was done, using Support Vector machines (SVM) as classification method, the accuracy is higher than 90%.

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Section: Related Workmentioning

confidence: 99%

“…Objects, places and events). However, recognition of an environmentally sound brings several challenges in comparison with existent recognition of music and speech techniques, because it must be considered that an environmentally sound (ES) is not structured by nature, typically contain noise and flat spectrum features 1 .…”

Section: Introductionmentioning

confidence: 99%

Feature Selection for Place Classification through Environmental Sounds

Delgado-Contreras

García-Vázquez

Brena

et al. 2014

Procedia Computer Science

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“…There are many strategies that can be followed in order to extract features from the audio records. Some examples are [ 8 , 9 ] where automatic audio detection and localization systems are described. Other examples are [ 10 , 11 ], where the authors presented an automatic acoustic recognition system for frog classification; Chunh et al .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of MPEG-7-Based Audio Descriptors for Animal Voice Recognition over Wireless Acoustic Sensor Networks

Luque

Larios

Personal

et al. 2016

Sensors

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Environmental audio monitoring is a huge area of interest for biologists all over the world. This is why some audio monitoring system have been proposed in the literature, which can be classified into two different approaches: acquirement and compression of all audio patterns in order to send them as raw data to a main server; or specific recognition systems based on audio patterns. The first approach presents the drawback of a high amount of information to be stored in a main server. Moreover, this information requires a considerable amount of effort to be analyzed. The second approach has the drawback of its lack of scalability when new patterns need to be detected. To overcome these limitations, this paper proposes an environmental Wireless Acoustic Sensor Network architecture focused on use of generic descriptors based on an MPEG-7 standard. These descriptors demonstrate it to be suitable to be used in the recognition of different patterns, allowing a high scalability. The proposed parameters have been tested to recognize different behaviors of two anuran species that live in Spanish natural parks; the Epidalea calamita and the Alytes obstetricans toads, demonstrating to have a high classification performance.

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“…But the number of samples inside their dataset is not substantial enough. Okuyucu et al [3] perform a feature and classifier analysis for the recognition of similar ES categories. Environmental sound categories are detected and tested, based on 11 audio features such as MPEG-7 family, MFCC, ZCR and their combinations by using HMM and SVM classifiers.…”

Section: Introductionmentioning

confidence: 99%

“…Recent research has shown that the performance of semantic audio event analysis can be improved through proper machine learning approaches [2], [3], [7], [9], [12]- [14]. To this end, two challenging issues that must be crucially taken into consideration for audio event detection are efficient feature representation as well as building effective machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Audio-based event detection in office live environments using optimized MFCC-SVM approach

Kucukbay

Sert

2015

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)

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Audio data contains several sounds and is an im portant source for multimedia applications. One of them is unstructured Environmental Sounds (also referred to as audio events) that have noise-like characteristics with flat spectrums. Therefore, in general, recognition methods applied for music and speech data are not appropriate for the Environmental Sounds. In this paper, we propose an MFCC-SVM based ap proach that exploits the effect of feature representation and learner optimization tasks for efficient recognition of audio events from audio signals. The proposed approach considers efficient representation of MFCC features using different window and hop sizes by changing the number of Mel coefficients in the analyses as well as optimizing the SVM parameters. Moreover, 16 different audio events from the IEEE Audio and Acoustic Signal Processing (AASP) Challenge Dataset, namely alert, clear throat, cough, door slam, drawer, keyboard, keys, knock, laughter, mouse, page turn, pen drop, phone, printer, speech, and switch that are collected from office live environments are utilized in the evaluations. Our empirical evaluations show that, when the results of the proposed methods are chosen for MFFC feature and SVM classifier, the tests conducted through using 5-fold cross validation gives the results of 62%, 58% and 55% for Precision, Recall and F-measure scores, respectively. Extensive experiments on audio-based event detection using the IEEE AASP Challenge dataset show the effectiveness of the proposed approach.

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Audio Feature and Classifier Analysis for Efficient Recognition of Environmental Sounds

Cited by 18 publications

References 15 publications

Feature Selection for Place Classification through Environmental Sounds

Feature Selection for Place Classification through Environmental Sounds

Evaluation of MPEG-7-Based Audio Descriptors for Animal Voice Recognition over Wireless Acoustic Sensor Networks

Audio-based event detection in office live environments using optimized MFCC-SVM approach

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