An audio encoder for encoding an audio signal having a lower frequency band and an upper frequency band includes: a detector for detecting a peak spectral region in the upper frequency band of the audio signal; a shaper for shaping the lower frequency band using shaping information for the lower band and for shaping the upper frequency band using at least a portion of the shaping information for the lower band, wherein the shaper is configured to additionally attenuate spectral values in the detected peak spectral region in the upper frequency band; and a quantizer and coder stage for quantizing a shaped lower frequency band and a shaped upper frequency band and for entropy coding quantized spectral values from the shaped lower frequency band and the shaped upper frequency band.

An automated music composition and generation process within an automated music composition and generation system driven by lyrical musical experience descriptors. The process involves the system user accessing said automated music composition and generation system, employing an automated music composition and generation engine having a system user interface. The system user interface is used to select and provide musical experience descriptors, including lyrics, to the automated music composition and generation engine for processing by said automated music composition and generation engine. The system user initiates the automated music composition and generation engine to compose and generate music based on the musical experience descriptors and lyrics provided.

An automated music composition and generation process within an automated music composition and generation system driven by lyrical musical experience descriptors. The process involves the system user accessing said automated music composition and generation system, employing an automated music composition and generation engine having a system user interface. The system user interface is used to select and provide musical experience descriptors, including lyrics, to the automated music composition and generation engine for processing by said automated music composition and generation engine. The system user initiates the automated music composition and generation engine to compose and generate music based on the musical experience descriptors and lyrics provided.

An automated music composition and generation process within an automated music composition and generation system driven by lyrics. The process involves the system user accessing said automated music composition and generation system, employing an automated music composition and generation engine having a system user interface. The system user interface is used to provide lyrics to the automated music composition and generation engine for processing by the automated music composition and generation engine. The system user initiates the automated music composition and generation engine to compose and generate music based on lyrics the provided as input. The lyrics are analyzed for vowel formants to generate pitch events, which are used to support the automated music composition process.

An automated music composition and generation process within an automated music composition and generation system driven by lyrics. The process involves the system user accessing said automated music composition and generation system, employing an automated music composition and generation engine having a system user interface. The system user interface is used to provide lyrics to the automated music composition and generation engine for processing by the automated music composition and generation engine. The system user initiates the automated music composition and generation engine to compose and generate music based on lyrics the provided as input. The lyrics are analyzed for vowel formants to generate pitch events, which are used to support the automated music composition process.

A computer may train a single-class machine learning using normal speech recordings. The machine learning model or any other model may estimate the normal range of parameters of a physical speech production model based on the normal speech recordings. For example, the computer may use a source-filter model of speech production, where voiced speech is represented by a pulse train and unvoiced speech by a random noise and a combination of the pulse train and the random noise is passed through an auto-regressive filter that emulates the human vocal tract. The computer leverages the fact that intentional modification of human voice introduces errors to source-filter model or any other physical model of speech production. The computer may identify anomalies in the physical model to generate a voice modification score for an audio signal. The voice modification score may indicate a degree of abnormality of human voice in the audio signal.

An encoder for encoding an audio signal has: an analyzer configured for deriving prediction coefficients and a residual signal from an unvoiced frame of the audio signal; a gain parameter calculator configured for calculating a first gain parameter information for defining a first excitation signal related to a deterministic codebook and for calculating a second gain parameter information for defining a second excitation signal related to a noise-like signal for the unvoiced frame; and a bitstream former configured for forming an output signal based on an information related to a voiced signal frame, the first gain parameter information and the second gain parameter information.

An encoder for encoding an audio signal has: an analyzer configured for deriving prediction coefficients and a residual signal from an unvoiced frame of the audio signal; a gain parameter calculator configured for calculating a first gain parameter information for defining a first excitation signal related to a deterministic codebook and for calculating a second gain parameter information for defining a second excitation signal related to a noise-like signal for the unvoiced frame; and a bitstream former configured for forming an output signal based on an information related to a voiced signal frame, the first gain parameter information and the second gain parameter information.

An automated music composition and generation system for automatically composing and generating digital pieces of music using an automated music composition and generation engine driven by a set of emotion-type and style-type musical experience descriptors and time and/or space parameters provided by a system user. The automated music composition and generation engine includes an instrument subsystem supporting a library of virtual instruments, wherein each virtual instrument is capable of performing one or more notes of at least a portion of the composed piece of music, in response to the emotion-type and/or style-type musical experience descriptors; an instrument selector subsystem for automatically selecting one or more of virtual instruments from the library, so that each selected virtual instrument performs one or more notes of at least a portion of the composed piece of music; and a digital piece creation subsystem for creating the digital piece of composed music by assembling the notes produced from the virtual instruments selected from the library.

An automated music composition and generation system for automatically composing and generating digital pieces of music using an automated music composition and generation engine driven by a set of emotion-type and style-type musical experience descriptors and time and/or space parameters provided by a system user. The automated music composition and generation engine includes an instrument subsystem supporting a library of virtual instruments, wherein each virtual instrument is capable of performing one or more notes of at least a portion of the composed piece of music, in response to the emotion-type and/or style-type musical experience descriptors; an instrument selector subsystem for automatically selecting one or more of virtual instruments from the library, so that each selected virtual instrument performs one or more notes of at least a portion of the composed piece of music; and a digital piece creation subsystem for creating the digital piece of composed music by assembling the notes produced from the virtual instruments selected from the library.