Abstract: It comprises analyzing audio content of multimedia files and performing a speech to text transcription thereof automatically by means of an ASR process, and selecting acoustic and language models adapted for the ASR process at least before the latter processes the multimedia file, i.e. “a priori”. The method is particularly applicable to the automatic indexing, aggregation and clustering of news from different sources and from different types of files, including text, audio and audiovisual documents without any manual annotation.

Abstract: A method for the detection of noise and speech segments in a digital audio input signal, the input signal being divided into a plurality of frames including a first stage in which a first classification of a frame as noise is performed if the mean energy value for this frame and the previous N frames is not greater than a first energy threshold, N>1, a second stage in which for each frame that has not been classified as noise in the first stage it is decided if the frame is classified as noise or as speech based on combining at least a first criterion of spectral similarity of the frame with acoustic noise and speech models, a second criterion of analysis of the energy of the frame and a third criterion of duration, and of using a state machine for detecting the beginning of a segment as an accumulation of a determined number of consecutive frames with acoustic similarity greater than a first threshold and for detecting the end of the segment; a third stage in which the classification as speech or as noise

Abstract: Method and system of identification of at least one audiovisual advertisement in a data stream, such as a digital television broadcasting, by detecting energy drops in an audio stream of the data stream and comparing a segment of the audio stream starting at the energy drop with an audio segment of the advertisement. The comparison step requires only a few seconds of data to perform the detection. Therefore, the identification of the advertisement is provided before the end of the advertisement in the data stream.

Abstract: It comprises analyzing audio content of multimedia files and performing a speech to text transcription thereof automatically by means of an ASR process, and selecting acoustic and language models adapted for the ASR process at least before the latter processes the multimedia file, i.e. “a priori”. The method is particularly applicable to the automatic indexing, aggregation and clustering of news from different sources and from different types of files, including text, audio and audiovisual documents without any manual annotation.

Abstract: Method and system of identification of at least one audiovisual advertisement in a data stream, such as a digital television broadcasting, by detecting energy drops in an audio stream of the data stream and comparing a segment of the audio stream starting at the energy drop with an audio segment of the advertisement. The comparison step requires only a few seconds of data to perform the detection. Therefore, the identification of the advertisement is provided before the end of the advertisement in the data stream.