Abstract: A method for modifying a sound signal, the method comprising: a step of obtaining time frames of the sound signal, in the frequency domain; for at least one time frame, applying a first transformation of the sound signal in the frequency domain, comprising: a step of extracting a spectral envelope of the sound signal for the at least one time frame; a step of calculating frequencies of formants of the spectral envelope; a step of modifying the spectral envelope of the sound signal, the modification comprising application of an increasing continuous transformation function of frequencies of the spectral envelope, parameterized by at least two frequencies of formants of the spectral envelope.

Abstract: Meta-data (tags) for an audiovisual file can be generated by producing an initial estimate of the tags and then revising the estimate (notably to expand it and/or render it more precise) based on the assumption that the relationships which hold between the different tags for a set of manually-tagged training examples will also hold for the tags of the input file now being tagged. A fully-automatic method and system is a hybrid between signal-based and machine-learning approaches, because the initial tag estimate is based on the physical properties of the signal representing the audiovisual file. The initial tag estimate may be produced by inferring that the input content will have the same tags as those files of the same kind, in the training database, which have a global similarity to the input audiovisual file in terms of signal properties.

Abstract: Meta-data (tags) for an audiovisual file can be generated by prompting a user to input certain tags (meta-data) descriptive of the audiovisual file, to serve as an initial estimate of the tags, and then revising the initial estimate (notably to expand it and/or render it more precise) based on the assumption that the relationships which hold between the different tags for a set of manually-tagged training examples will also hold for the tags of the input file now being tagged.

Abstract: Meta-data (tags) for an audiovisual file can be generated by prompting a user to input certain tags (meta-data) descriptive of the audiovisual file, to serve as an initial estimate of the tags, and then revising the initial estimate (notably to expand it and/or render it more precise) based on the assumption that the relationships which hold between the different tags for a set of manually-tagged training examples will also hold for the tags of the input file now being tagged.

Abstract: Audio samples corresponding to audio extracts or whole audio titles are automatically mapped to triggers 12 in a playable sound-producing device 1, the mapping being dependent on the meta-data associated with the audio samples. Thus, a user can play the sound-producing device and generate sounds derived from his favorite audio titles. It is possible to define different mappings between the audio samples and the playable domain of the sound-producing device: an audio sample selector 50 can select between different possible samples for playback by comparing the audio properties of the samples and the play-mode and/or characteristics of the user's performance. An audio sampler/sample-processor 70 can automatically extract segments of an audio source file and map them to triggers in the sound-producing device 1.

Abstract: Meta-data (tags) for an audiovisual file can be generated by producing an initial estimate of the tags and then revising the estimate (notably to expand it and/or render it more precise) based on the assumption that the relationships which hold between the different tags for a set of manually-tagged training examples will also hold for the tags of the input file now being tagged. A fully-automatic method and system is a hybrid between signal-based and machine-learning approaches, because the initial tag estimate is based on the physical properties of the signal representing the audiovisual file. The initial tag estimate may be produced by inferring that the input content will have the same tags as those files of the same kind, in the training database, which have a global similarity to the input audiovisual file in terms of signal properties.

Abstract: Audio samples corresponding to audio extracts or whole audio titles are automatically mapped to triggers 12 in a playable sound-producing device 1, the mapping being dependent on the meta-data associated with the audio samples. Thus, a user can play the sound-producing device and generate sounds derived from his favourite audio titles. It is possible to define different mappings between the audio samples and the playable domain of the sound-producing device: an audio sample selector 50 can select between different possible samples for playback by comparing the audio properties of the samples and the play-mode and/or characteristics of the user's performance. An audio sampler/sample-processor 70 can automatically extract segments of an audio source file and map them to triggers in the sound-producing device 1.