The use of kernel methods for audio events detection

Nasser, Alissar; Hamad, Denis; Rouas, Jean-Luc; Ambellouis, Sébastien

doi:10.1109/ictta.2008.4529996

Cited by 3 publications

(2 citation statements)

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“…A logarithm transformation is applied to the filter bank outputs followed by Discrete Cosine Transform which consequently generates 12 cepstrum coefficients. To detect the spectrum relationships between neighboring frames, first and second derivatives of the 12 attributes plus three terms of energy are added to end up with 39 MFCC attributes [11,14].…”

Section: Mfcc Feature Extractionmentioning

confidence: 99%

Security Detection in Audio Events: A Comparison of Classification Methods

Nasser

2020

JAMCS

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The security of public places is becoming important with the increased rate of violence and subversion. Recently, several types of research have been proposed to automatically detect abnormal behavior in public places like a car crash, violence or other hazardous events in an attempt to improve security and save lives. Furthermore, most of the researches are using supervised classifications techniques to classify the audio signals. This paper proposes the use of the kernel principal component analysis (KPCA) to reduce the number of MFCC features extracted from the audio signal and then apply an unsupervised classification algorithm. Moreover, this paper presents the results of several supervised and unsupervised classification methods for audio events detection and compares these results with the result of the proposed approach. Experiments are done using a real data set recorded at the mean of public transportation. The obtained results reveal that K-means on 2 KPCA components gave good results for triggering a true alarm as well as detecting a false alarm; where the percentages of false and missed alarms were 4.5% and 7.8% respectively; whereas these values were 0.8% and 9.3% respectively for kernel k-means. Notwithstanding the DNN network gave the best results with a false alarm rate of 0% and 1.4% missed alarm.

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Section: Mfcc Feature Extractionmentioning

confidence: 99%

Security Detection in Audio Events: A Comparison of Classification Methods

Nasser

2020

JAMCS

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“…Kernel-based methods for multimedia retrieval have shown their robustness for many tasks, in shape recognition [37], image retrieval [14], or event detection [29] for instance. Most methods first build a kernel function, usually from supervised data [5], then train a classifier such as Support Vector Machines (SVM).…”

Section: Introductionmentioning

confidence: 99%

Incremental kernel learning for active image retrieval without global dictionaries

Gosselin

Precioso

Philipp‐Foliguet

2011

Pattern Recognition

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In content-based image retrieval context, a classic strategy consists in computing off-line a dictionary of visual features. This visual dictionary is then used to provide a new representation of the data which should ease any task of classification or retrieval. This strategy, based on past research works in text retrieval, is suitable for the context of batch learning, when a large training set can be built either by using a strong prior knowledge of data semantics (like for textual data) or with an expensive off-line pre-computation. Such an approach has major drawbacks in the context of interactive retrieval, where the user iteratively builds the training data set in a semi-supervised approach by providing positive and negative annotations to the system in the relevance feedback loop. The training set is thus built for each retrieval session without any prior knowledge about the concepts of interest for this session. We propose a completely different approach to build the dictionary on-line from features extracted in relevant images. We design the corresponding kernel function, which is learnt during the retrieval session. For each new label, the kernel function is updated with a complexity linear with respect to the size of the database. We propose an efficient active learning strategy for the weakly supervised retrieval method developed in this paper. Moreover this framework allows the combination of features of different types. Experiments are carried out on standard databases, and show that a small dictionary can be dynamically extracted from the features with better performances than a global one.

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Detection and visualisation of outliers using kernel principal components

Nasser

Hamad²

2015

2015 Fifth International Conference on Digital Information and Communication Technology and Its Applications (DICTAP)

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The use of kernel methods for audio events detection

Cited by 3 publications

References 4 publications

Security Detection in Audio Events: A Comparison of Classification Methods

Security Detection in Audio Events: A Comparison of Classification Methods

Incremental kernel learning for active image retrieval without global dictionaries

Detection and visualisation of outliers using kernel principal components

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