Abstract: Techniques and tools related to coding and decoding of audio information are described. For example, redundant coded information for decoding a current frame includes signal history information associated with only a portion of a previous frame. As another example, redundant coded information for decoding a coded unit includes parameters for a codebook stage to be used in decoding the current coded unit only if the previous coded unit is not available. As yet another example, coded audio units each include a field indicating whether the coded unit includes main encoded information representing a segment of an audio signal, and whether the coded unit includes redundant coded information for use in decoding main encoded information.

Abstract: A transform coder is described that performs a time-split transform in addition to a discrete cosine type transform. A time-split transform is selectively performed based on characteristics of media data. Transient detection identifies a changing signal characteristic, such as a transient in media data. After encoding an input signal from a time domain to a transform domain, a time-splitting transformer selectively perform an orthogonal sum-difference transform on adjacent coefficients indicated by a changing signal characteristic location. The orthogonal sum-difference transform on adjacent coefficients results in transforming a vector of coefficients in the transform domain as if they were multiplied by an identity matrix including at least one 2×2 time-split block along a diagonal of the matrix. A decoder performs an inverse of the described transforms.

Abstract: Techniques and tools for representing, coding, and decoding scale factor information are described herein. For example, during encoding of scale factors, an encoder uses one or more of flexible scale factor resolution selection, spatial prediction of scale factors, flexible prediction of scale factors, smoothing of noisy scale factor amplitudes, reordering of scale factor prediction residuals, and prediction of scale factor prediction residuals. Or, during decoding, a decoder uses one or more of flexible scale factor resolution selection, spatial prediction of scale factors, flexible prediction of scale factors, reordering of scale factor prediction residuals, and prediction of scale factor prediction residuals.

Abstract: Frequency segmentation is important to the quality of encoding spectral data. Segmentation involves breaking the spectral data into units called sub-bands or vectors. Homogeneous segmentation may be suboptimal. Various features are described for providing spectral data intensity dependent segmentation. Finer segmentation is provided for regions of greater spectral variance and coarser segmentation is provided for more homogeneous regions. Sub-bands which have similar characteristics may be merged with very little effect on quality, whereas sub-bands with highly variable data may be better represented if a sub-band is split. Various methods are described for measuring tonality, energy, or shape of a sub-band. These various measurements are discussed in light of making decisions of when to split or merge sub-bands to provide variable frequency segmentation.

Abstract: Techniques and tools for reordering of spectral coefficients in encoding and decoding are described herein. For certain types and patterns of content, coefficient reordering reduces redundancy that is due to periodic patterns in the spectral coefficients, making subsequent entropy encoding more efficient. For example, an audio encoder receives spectral coefficients logically organized along one dimension such as frequency, reorders at least some of the spectral coefficients, and entropy encodes the spectral coefficients after the reordering. Or, an audio decoder receives entropy encoded information for such spectral coefficients, entropy decodes the information, and reverses reordering of at least some of the spectral coefficients.

Abstract: Techniques and tools for selectively using multiple entropy models in adaptive coding and decoding are described herein. For example, for multiple symbols, an audio encoder selects an entropy model from a first model set that includes multiple entropy models. Each of the multiple entropy models includes a model switch point for switching to a second model set that includes one or more entropy models. The encoder processes the multiple symbols using the selected entropy model and outputs results. Techniques and tools for generating entropy models are also described.

Abstract: Techniques and tools for prediction of spectral coefficients in encoding and decoding are described herein. For certain types and patterns of content, coefficient prediction exploits correlation between adjacent spectral coefficients, making subsequent entropy encoding more efficient. For example, an audio encoder predictively codes quantized spectral coefficients in the quantized domain and entropy encodes results of the predictive coding. Or, for a particular quantized spectral coefficient, an audio decoder entropy decodes a difference value, computes a predictor in the quantized domain, and combines the predictor and the difference value.

Abstract: Coding of spectral data by representing certain portions of the spectral data as a scaled version of a code-vector, where the code-vector is chosen from either a fixed predetermined codebook or a codebook taken from a baseband. Various optional features are described for modifying the code-vectors in the codebook according to some rules which allow the code-vector to better represent the data they are modeling. The code-vector modification comprises a linear or non-linear transform of one or more code-vectors, such as, by exponentiation, negation, reversing, or combining elements from plural code-vectors.

Abstract: Quantization matrices facilitate digital audio encoding and decoding. An audio encoder generates and compresses quantization matrices; an audio decoder decompresses and applies the quantization matrices. For example, the audio encoder includes a multi-channel transformer operable to output multi-channel audio data in jointly coded channels and a program module for generating a single quantization matrix for weighting all of the jointly coded channels. In one such example, the program module computes the single quantization matrix from an aggregation of pattern information for all of the jointly coded channels, and the aggregation of pattern information is an aggregate excitation pattern.

Abstract: An audio processing tool measures the quality of reconstructed audio data. For example, an audio encoder measures the quality of a block of reconstructed frequency coefficient data in a quantization loop. The invention includes several techniques and tools, which can be used in combination or separately. First, before measuring quality, the tool normalizes the block to account for variation in block sizes. Second, for the quality measurement, the tool processes the reconstructed data by critical bands, which can differ from the quantization bands used to compress the data. Third, the tool accounts for the masking effect of the reconstructed data, not just the masking effect of the original data. Fourth, the tool band weights the quality measurement, which can be used to account for noise substitution or band truncation. Finally, the tool changes quality measurement techniques depending on the channel coding mode.

Abstract: Techniques and tools related to delayed or lost coded audio information are described. For example, a concealment technique for one or more missing frames is selected based on one or more factors that include a classification of each of one or more available frames near the one or more missing frames. As another example, information from a concealment signal is used to produce substitute information that is relied on in decoding a subsequent frame. As yet another example, a data structure having nodes corresponding to received packet delays is used to determine a desired decoder packet delay value.

Abstract: Techniques and tools related to coding and decoding of audio information are described. For example, redundant coded information for decoding a current frame includes signal history information associated with only a portion of a previous frame. As another example, redundant coded information for decoding a coded unit includes parameters for a codebook stage to be used in decoding the current coded unit only if the previous coded unit is not available. As yet another example, coded audio units each include a field indicating whether the coded unit includes main encoded information representing a segment of an audio signal, and whether the coded unit includes redundant coded information for use in decoding main encoded information.

Abstract: Techniques and tools are described for processing reconstructed audio signals. For example, a reconstructed audio signal is filtered in the time domain using filter coefficients that are calculated, at least in part, in the frequency domain. As another example, producing a set of filter coefficients for filtering a reconstructed audio signal includes clipping one or more peaks of a set of coefficient values. As yet another example, for a sub-band codec, in a frequency region near an intersection between two sub-bands, a reconstructed composite signal is enhanced.

Abstract: Techniques and tools related to delayed or lost coded audio information are described. For example, a concealment technique for one or more missing frames is selected based on one or more factors that include a classification of each of one or more available frames near the one or more missing frames. As another example, information from a concealment signal is used to produce substitute information that is relied on in decoding a subsequent frame. As yet another example, a data structure having nodes corresponding to received packet delays is used to determine a desired decoder packet delay value.

Abstract: Techniques and tools related to coding and decoding of audio information are described. For example, redundant coded information for decoding a current frame includes signal history information associated with only a portion of a previous frame. As another example, redundant coded information for decoding a coded unit includes parameters for a codebook stage to be used in decoding the current coded unit only if the previous coded unit is not available. As yet another example, coded audio units each include a field indicating whether the coded unit includes main encoded information representing a segment of an audio signal, and whether the coded unit includes redundant coded information for use in decoding main encoded information.

Abstract: Quantization matrices facilitate digital audio encoding and decoding. An audio encoder generates and compresses quantization matrices; an audio decoder decompresses and applies the quantization matrices. For example, the audio encoder generates a quantization matrix including weighting factors and processes a set of weighting factors according to a parametric model to switch between a direct representation and a parametric representation of the set of weighting factors, where the parametric representation of the set of weighting factors accounts for audibility of distortion according to a model of human auditory perception. In another example, an audio encoder receives a band weight representation of a ciuantization matrix and compresses the band weight representation of the quantization matrix using linear predictive coding, wherein the compressing includes computing pseudo-autocorrelation values for the quantization matrix.

Abstract: An audio processing tool measures the quality of reconstructed audio data. For example, an audio encoder measures the quality of a block of reconstructed frequency coefficient data in a quantization loop. The invention includes several techniques and tools, which can be used in combination or separately. First, before measuring quality, the tool normalizes the block to account for variation in block sizes. Second, for the quality measurement, the tool processes the reconstructed data by critical bands, which can differ from the quantization bands used to compress the data. Third, the tool accounts for the masking effect of the reconstructed data, not just the masking effect of the original data. Fourth, the tool band weights the quality measurement, which can be used to account for noise substitution or band truncation. Finally, the tool changes quality measurement techniques depending on the channel coding mode.

Abstract: An audio processing tool measures the quality of reconstructed audio data. For example, an audio encoder measures the quality of a block of reconstructed frequency coefficient data in a quantization loop. The invention includes several techniques and tools, which can be used in combination or separately. First, before measuring quality, the tool normalizes the block to account for variation in block sizes. Second, for the quality measurement, the tool processes the reconstructed data by critical bands, which can differ from the quantization bands used to compress the data. Third, the tool accounts for the masking effect of the reconstructed data, not just the masking effect of the original data. Fourth, the tool band weights the quality measurement, which can be used to account for noise substitution or band truncation. Finally, the tool changes quality measurement techniques depending on the channel coding mode.

Abstract: A compressed digital audio signal is transmitted from an audio source along a connection wire to an audio receiver. The digital audio signal can encode digital audio data having different sampling frequencies, frames sizes, and other information. The audio receiver that receives the digital audio signal can decode and convert the compressed digital audio signal into multiple synchronized analog signals, which are used to drive multiple speakers. The audio receiver may also synchronize the audio data with associated video data so that the audio playback and video playback are “in sync”, despite delay introduced by the audio signal decoding at the audio receiver.

Abstract: A transcoder reduces excess requantization error in quantization of spectral data. The transcoder phase shifts data decompressed by a decompressor. The phase shifting causes a change to corresponding spectral data produced in later transform coding of the decompressed data. When the spectral data is then quantized to reduce bitrate, the earlier phase shifting reduces excess requantization error. After transcoding, a second decompressor can compensate for the phase shifting by, for example, reverse shifting by the amount of the phase shift. Instead of phase shifting, the transcoder can reduce excess requantization error by, for example, adding random noise to the decompressed data or changing transform block sizes.