A voice processing method for estimating an impression of speech includes: executing an acquisition process that includes acquiring voice signals; executing a feature acquisition process that includes acquiring acoustic features regarding the voice signals from the voice signals; executing a voice-parameter acquisition process that includes acquiring a voice parameter regarding a frame of the voice signals; executing a relative-value determination process that includes determining a relative value between the determined voice parameter and a statistical value of the voice parameter; executing a weight assignment process that includes assigning a weight to the frame of the voice signals in accordance with the relative value; and executing a distribution determination process that includes determining a distribution of the acoustic features, based on the weight assigned to the frame of the voice signals.

A pitch information generation device includes a first envelope generator configured to generate, with regard to a first sound range, a first envelope that attenuates at a first rate of change from a detected value corresponding to a peak in the sound signal, a second envelope generator configured to generate, with regard to a second sound range, which includes a sound range of higher frequency than the first sound range, a second envelope that attenuates from a detected value corresponding to a peak in the sound signal at a second rate of change. The second rate of change is greater than the first rate of change. A pitch information identifier is configured to identify the pitch information based on the first envelope and the second envelope.

The present invention is used for automatically generating a sensitive/emotional keyword that suits a sound source, and a method for generating a keyword for a sound source according to the present invention includes: collecting text data related to a target sound source from one or more websites; extracting one or more waveform patterns from the target sound source; generating music information for the target sound source by using the waveform pattern; determining weights of the text data and the music information according to a genre of the target sound source; and generating a keyword to be tagged to the target sound source by selectively using at least one of the text data and the music information according to the determined weights.

An automated music composition and generation system including a system user interface for enabling system users to review and select one or more musical experience descriptors, as well as time and/or space parameters; and an automated music composition and generation engine, operably connected to the system user interface, for receiving, storing and processing musical experience descriptors and time and/or space parameters selected by the system user, so as to automatically compose and generate one or more digital pieces of music in response to the musical experience descriptors and time and/or space parameters selected by the system user. The automated music composition and generation engine includes: a digital piece creation subsystem for creating and delivering the digital piece of music to the system user interface; and a digital audio sample producing subsystem supported by virtual musical instrument libraries.

An automated music composition and generation system including a system user interface for enabling system users to review and select one or more musical experience descriptors, as well as time and/or space parameters; and an automated music composition and generation engine, operably connected to the system user interface, for receiving, storing and processing musical experience descriptors and time and/or space parameters selected by the system user, so as to automatically compose and generate one or more digital pieces of music in response to the musical experience descriptors and time and/or space parameters selected by the system user. The automated music composition and generation engine includes: a digital piece creation subsystem for creating and delivering the digital piece of music to the system user interface; and a digital audio sample producing subsystem supported by virtual musical instrument libraries.

Described embodiments include apparatus that includes a network interface (28) and a processor (30). The processor is configured to receive, via the network interface, speech of a subject (22) who suffers from a pulmonary condition related to accumulation of excess fluid, to identify, by analyzing the speech, one or more speech-related parameters of the speech, to assess, in response to the speech-related parameters, a status of the pulmonary condition, and to generate, in response thereto, an output indicative of the status of the pulmonary condition. Other embodiments are also described.

Described embodiments include apparatus that includes a network interface (28) and a processor (30). The processor is configured to receive, via the network interface, speech of a subject (22) who suffers from a pulmonary condition related to accumulation of excess fluid, to identify, by analyzing the speech, one or more speech-related parameters of the speech, to assess, in response to the speech-related parameters, a status of the pulmonary condition, and to generate, in response thereto, an output indicative of the status of the pulmonary condition. Other embodiments are also described.

There is provided an audio processing device including a memory, and a processor coupled to the memory and the processor configured to detect a first acoustic feature amount and a second acoustic feature amount of an input audio, calculate a coefficient for the second acoustic feature amount based on a time change amount by calculating the time change amount of the first acoustic feature amount, and calculate a statistical amount for the second acoustic feature amount based on the coefficient.

A weighting function determination method includes obtaining a line spectral frequency (LSF) coefficient or an immitance spectral frequency (ISF) coefficient from a linear predictive coding (LPC) coefficient of an input signal and determining a weighting function by combining a first weighting function based on spectral analysis information and a second weighting function based on position information of the LSF coefficient or the ISF coefficient.

A weighting function determination method includes obtaining a line spectral frequency (LSF) coefficient or an immitance spectral frequency (ISF) coefficient from a linear predictive coding (LPC) coefficient of an input signal and determining a weighting function by combining a first weighting function based on spectral analysis information and a second weighting function based on position information of the LSF coefficient or the ISF coefficient.