Category : Signal Processing

Audio Analysis Machine Learning Publications Signal Processing Video Analysis

Artificial Intelligence and Video Mining: Audio Event Detection Using SVM

In this paper we present a method aiming at analyzing the content an audio signal by using an artificial intelligence technique: Support Vector Machines (SVM). The objective is to detect the different events occurring in an unknown audio signal for information retrieval purposes. We present particularly the detection of violent events in a video. 

There are two types of data mining, depending on whether the aim is to describe or rather to predict. In the specific case of audio data mining, on the one hand there is a descriptive method consisting of classifying a set of audio signals into the most similar groups of signals from a perception viewpoint. This is unsupervised classification. On the other hand, there is the predictive method consisting in designing a model from a learning database. In this way, any new audio signal could be automatically classified on the basis of the built model. This method is the supervised classification. The present paper deals with the supervised classification.

There are various supervised classification algorithms, such as decision trees, neurone networks, etc. However, we chose Support Vector Machine (SVM) which, according to the literature gives good results for real-world applications.

Firstly, we will describe the database or corpus. In a second section, we will present features used to describe the stimuli of the corpus. The third part of the paper will be devoted to brief theory on SVM algorithm. Finally, we will present the results of our study before drawing conclusions from this work.

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Audio Analysis Publications Signal Processing Speech Processing

Signal Processing Applied To Video Mining: Video Boundaries Detection

Scene change detection is a technique which aims to identify automatically the scene change in a video. Assuming that a scene is defined by its audio and video signals, we present here scene change techniques based on audio and video signals. In the case of audio signal, the different techniques are based on abrupt variations of their frequency- and time-based features. For techniques based on video signals, the usual algorithms are based on the Sum of Absolute Differences (SAD) variation.

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Audio Analysis Machine Learning Publications Signal Processing Video Analysis

Random Forest Classifier and Bag of Audio Words concept applied to audio scene recognition

Bag of Audio Words (BoAW) is a concept inspired by the text mining research area. The idea is to represent any audio signal as a document of words. In this parallelism, each word corresponds to an acoustic feature.  The concept was successfully applied to image processing where the bag of visual words is generated using an unsupervised classifier like k-means. Here we will describe how to design a Bag of Words for the speech/audio signal case. Since the final goal is to build an audio/speech pattern recognition system, we will used as supervised classifier the Random Forest  (RF) classifier, which is well adapted to large data sets with a very high number of features. Moreover, it has some good robustness properties to guard against overfitting.

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Audio Analysis Information Retrieval Machine Learning Publications Signal Processing Speech Processing Video Analysis

Automatic Emotion Recognition system using Deep Belief Network

Mood is a subjective term describing the emotional state of a human being. It can be expressed in textual form (e.g. twitter …). Let us remember that this topic is already addressed in our paper about sentiment analyses. On the other hand, mood can be recognized by analyzing facial expressions or/and the nature of voice. The speech-based Automatic Emotion Recognition (AER) systems which will be discussed here have several types of application, such as emotion detection in call centers, where being able to detect the emotion can be helpful in taking appropriate decisions. In the case of online video advertising, forecasting the emotion from speech signals in video can be useful to fine-tune the user targeting. Obviously, emotion detected from speech can be combined with facial expressions and textual information to improve accuracy. Here we will focus on Automatic Emotion Recognition based uniquely on an analysis of human speech. The system that will be presented is based on a recent machine learning technique: Deep Learning Network (DBN). It is an improvement on classical neural networks. We will describe the DBN and the database of emotional speech used to build such an AER system.

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Information Retrieval Publications Signal Processing Speech Processing Speech Recognition

Keyword Spotting

Keyword spotting (KWS) or Spoken Term Detection (SPT) is a subcategory of Automatic Speech Recognition (ASR). Contrarily to ASR whose objective is to transcribe a speech in its entirety, KWS must detect only a predefined set of words.

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