Abstract: The present invention provides for methods and apparatuses for processing audio data. In one embodiment, there is provided a method for achieving bitstream scalability in a multi-channel audio encoder, said method comprising receiving audio input data; organizing said input data by a Code Excited Linear Predictor (CELP) processing module for further encoding by arranging said data according to significance of data, where more significant data is placed ahead of less significant data; and providing a scalable output bitstream; a higher bitrate bitstream is scaled to lower bitrate by discarding less significant data from frame ends. The organized CELP data comprises of a first part and a second part. The first part comprises a frame header, sub frame parameters and innovation vector quantization data from the first frame from all channels. The innovation vector quantization data from the first frames from all channels is arranged according to channel number.

Abstract: The present invention provides for methods and apparatuses for processing audio data. In one embodiment, there is a provided a method for achieving bitstream scalability in a multi-channel audio encoder, said method comprising receiving audio input data; organizing said input data by a Code Excited Linear Predictor (CELP) processing module for further encoding by arranging said data according to significance of data, where more significant data is placed ahead of less significant data; and providing a scalable output bitstream. The organized CELP data comprises of a first part and a second part. The first part comprises a frame header, sub frame parameters and innovation vector quantization data from the first frame from all channels. The innovation vector quantization data from the first frames from all channels is arranged according to channel number.

Abstract: A low-cost, real-time solution is presented for compensating memoryless non-linear distortion in an audio transducer. The playback audio system estimates signal amplitude and velocity, looks up a scale factor from a look-up table (LUT) for the defined pair (amplitude, velocity) (or computes the scale factor for a polynomial approximation to the LUT), and applies the scale factor to the signal amplitude. The scale factor is an estimate of the transducer's memoryless nonlinear distortion at a point in its phase plane given by (amplitude, velocity), which is found by applying a test signal having a known signal amplitude and velocity to the transducer, measuring a recorded signal amplitude and setting the scale factor equal to the ratio of the test signal amplitude to the recorded signal amplitude. Scaling can be used to either pre- or post-compensate the audio signal depending on the audio transducer.

Abstract: Digital audio samples are represented as a product of scale factors codes and corresponding quantity codes, sometimes referred to as exponent/mantissa format. To compress audio data, scale factors are organized by sample time and frequency either by filtering or frequency transformation, into a two-dimensional frame. The frame may be decomposed into “tiles” by partition. One or more such scale factor tiles are compressed by transformation by a two-dimensional, orthogonal transformation such as a two dimensional discrete cosine transform. Optional further encoding is applied to reduce redundancy. A decoding method and an encoded machine readable medium complement the method of encoding.

Abstract: Digital audio samples are represented as a product of scale factors codes and corresponding quantity codes, sometimes referred to as exponent/mantissa format. To compress audio data, scale factors are organized by sample time and frequency either by filtering or frequency transformation, into a two-dimensional frame. The frame may be decomposed into “tiles” by partition. One or more such scale factor tiles are compressed by transformation by a two-dimensional, orthogonal transformation such as a two dimensional discrete cosine transform. Optional further encoding is applied to reduce redundancy. A decoding method and an encoded machine readable medium complement the method of encoding.

Abstract: Neural networks provide efficient, robust and precise filtering techniques for compensating linear and non-linear distortion of an audio transducer such as a speaker, amplified broadcast antenna or perhaps a microphone. These techniques include both a method of characterizing the audio transducer to compute the inverse transfer functions and a method of implementing those inverse transfer functions for reproduction. The inverse transfer functions are preferably extracted using time domain calculations such as provided by linear and non-linear neural networks, which more accurately represent the properties of audio signals and the audio transducer than conventional frequency domain or modeling based approaches. Although the preferred approach is to compensate for both linear and non-linear distortion, the neural network filtering techniques may be applied independently.

Abstract: A method for compressing audio input signals to form a master bit stream that can be scaled to form a scaled bit stream having an arbitrarily prescribed data rate. A hierarchical filterbank decomposes the input signal into a multi-resolution time/frequency representation from which the encoder can efficiently extract both tonal and residual components. The components are ranked and then quantized with reference to the same masking function or different psychoacoustic criteria. The selected tonal components are suitably encoded using differential coding extended to multichannel audio. The time-sample and scale factor components that make up the residual components are encoded using joint channel coding (JCC) extended to multichannel audio. A decoder uses an inverse hierarchical filterbank to reconstruct the audio signals from the tonal and residual components in the scaled bit stream.

Abstract: A low-cost, real-time solution is presented for compensating memoryless non-linear distortion in an audio transducer. The playback audio system estimates signal amplitude and velocity, looks up a scale factor from a look-up table (LUT) for the defined pair (amplitude, velocity) (or computes the scale factor for a polynomial approximation to the LUT), and applies the scale factor to the signal amplitude. The scale factor is an estimate of the transducer's memoryless nonlinear distortion at a point in its phase plane given by (amplitude, velocity), which is found by applying a test signal having a known signal amplitude and velocity to the transducer, measuring a recorded signal amplitude and setting the scale factor equal to the ratio of the test signal amplitude to the recorded signal amplitude. Scaling can be used to either pre- or post-compensate the audio signal depending on the audio transducer.

Abstract: Neural networks provide efficient, robust and precise filtering techniques for compensating linear and non-linear distortion of an audio transducer such as a speaker, amplified broadcast antenna or perhaps a microphone. These techniques include both a method of characterizing the audio transducer to compute the inverse transfer functions and a method of implementing those inverse transfer functions for reproduction. The inverse transfer functions are preferably extracted using time domain calculations such as provided by linear and non-linear neural networks, which more accurately represent the properties of audio signals and the audio transducer than conventional frequency domain or modeling based approaches. Although the preferred approach is to compensate for both linear and non-linear distortion, the neural network filtering techniques may be applied independently.