Classification of audio events in broadcast news
نویسندگان
چکیده
This paper describes our approach to discriminate news report from others such as commercials and music in broadcast news programs based on audio information. The reported work here is part of the effort at AT&T to hierarchically segment broadcast news programs into semantically meaningful units at different levels of abstraction. At the coarse level, using the described approach we preprocess the audio data to pass only the news segments as input to a speaker identification system. To develop a lightweight preprocessing scheme for efficiency, we adopted a set of audio features that are simple to compute yet, based on our observation, statistically capture the intrinsic properties of the audio events to be classified. To improve the performance of the classifier, fuzzy membership functions associated with the features are introduced. Preliminary experimental results are reported which demonstrate the usefulness of the approach.
منابع مشابه
Automatic Sound Classification of Radio Broadcast News
Automatic extraction of the index of broadcast streams from radio and television has become a challenging research topic over the last years. The automatic classification of audio types, such as speech, music, noises/atypical events etc, has found numerous applications. In this paper we study the evaluation of different machine learning algorithms, which have successfully been used in other cla...
متن کاملSpectral cross-correlation features for audio indexing of broadcast news and meetings
This paper describes the effect of three new acoustic feature parameters to detect audio source segments that are based on spectral cross-correlation: spectral stability, white noise similarity, and sound spectral shape. These parameters are devised for accurate audio source detection and are used in a pre-processing module for automatic indexing of the broadcast news and the meetings. We condu...
متن کاملSpeaker tracking in a broadcast news corpus
Speaker tracking is the process of following who says something in an audio stream. In the case the audio stream is a recording of broadcast news, speaker identity can be an important meta-data for building digital libraries. Moreover, the segmentation and classification of the audio stream in terms of acoustic contents, bandwidth and speaker gender allow to filter out portions of the signal wh...
متن کاملA Stream-based Audio Segmentation, C Pre-processing System for Broadcast
This paper describes our work on the development of a low latency stream-based audio pre-processing system for broadcast news using model-based techniques. It performs speech/nonspeech classification, speaker segmentation, speaker clustering, gender and background conditions classification. As a way to increase the modelling accuracy our algorithms make extensive use of Artificial Neural Networ...
متن کاملData-Driven Audio Feature Space Clustering for Automatic Sound Recognition in Radio Broadcast News
Aiming to an automatic sound recognizer for radio broadcasting events, a methodology of clustering the audio feature space using the discrimination ability of the audio descriptors as a criterion, is investigated in this work. From a given and close set of audio events, commonly found in broadcast news transmissions, a large set of audio descriptors is extracted and their data-driven ranking of...
متن کامل