Preface. 1. Introduction. 2. Signal Processing Essentials. 3. Quantization and Entropy Coding. 4. Linear Prediction in Narrowband and Wideband Coding. 5. Psychoacoustic Principles. 6. Time-Frequency Analysis: Filter Banks and Transforms. 7. Transform Coders. 8. Subband Coders. 9. Sinusoidal Coders. 10. Audio Coding Standards and Algorithms. 11. Lossless Audio Coding and Digital Watermarking. 12. Quality Measures for Perceptual Audio Coding. References. Index.

http://jdsp.engineering.asu.edu/books/cover-page-audio+contents.pdf

Audio Signal Processing and Coding

Circuits and methods for providing zero-gap playback of consecutive data streams in portable electronic devices, such as media players, are described. In some embodiments, a circuit includes a decoder circuit configured to receive encoded audio data and to output decoded audio data including data streams associated with a data file and a subsequent data file. Moreover, a predictive circuit, which is electrically coupled to the decoder circuit, is configured to selectively generate additional samples based on samples in the data file, where the additional samples correspond to times after the end of a data stream associated with the data file. Additionally, a filter circuit, which is electrically coupled to the decoder circuit and selectively electrically coupled to the predictive circuit, is configured to selectively combine or blend samples at a beginning of the subsequent data file with the additional samples. Note that the circuit may be included in an integrated circuit.

Zero-gap playback using predictive mixing

An echo canceller can be arranged to receive an input signal and to receive a reference signal. The echo canceller can subtract a linear component of the reference signal from the input signal. A noise suppressor can suppress non-linear effects of the reference signal in the input signal in correspondence with a large number of selectable parameters. Such suppression can be provided on a frequency-by-frequency basis, with a unique set of tunable parameters selected for each frequency. A degree of suppression provided by the noise suppressor can correspond to an estimate of residual echo remaining after the one or more linear components of the reference signal have been subtracted from the input signal, to an estimated double-talk probability, and to an estimated signal-to-noise ratio of near-end speech in the input signal for each respective frequency. A speech recognizer can receive a processed input signal from the noise suppressor.

Voice quality enhancement techniques, speech recognition techniques, and related systems

An audio encoder has a window function controller, a windower, a time warper with a final quality check functionality, a time/frequency converter, a TNS stage or a quantizer encoder, the window function controller, the time warper, the TNS stage or an additional noise filling analyzer are controlled by signal analysis results obtained by a time warp analyzer or a signal classifier. Furthermore, a decoder applies a noise filling operation using a manipulated noise filling estimate depending on a harmonic or speech characteristic of the audio signal.

Time warp activation signal provider, audio signal encoder, method for providing a time warp activation signal, method for encoding an audio signal and computer programs

A technique is described for concealing the effect of a lost frame in a series of frames representing an encoded audio signal in a sub-band predictive coding system. In accordance with the technique, a first synthesized sub-band audio signal is synthesized, wherein synthesizing the first synthesized sub-band audio signal comprises performing waveform extrapolation based on a stored first sub-band decoded audio signal. A second synthesized sub-band audio signal is also synthesized, wherein synthesizing the second synthesized sub-band audio signal comprises performing waveform extrapolation based on the stored second sub-band decoded audio signal. The first synthesized sub-band audio signal and the second synthesized sub-band audio signal are combined to generate a synthesized full-band output audio signal corresponding to a lost frame.

Packet loss concealment for sub-band predictive coding based on extrapolation of sub-band audio waveforms

During the years 1999 and 2000, the Telecommunication Industry Association (TIA) and the 3rd Generation Partnership Project 2 (3GPP2), managed a competition and a selection process for a new speech coding standard for CDMA applications. The new speech coding standard, which is coined Selectable Mode Vocoder (SMV), will become a service option in CDMA systems such as IS-95 and cdma2000, providing a higher quality, flexibility, and capacity over the existing speech coding service options, IS-96C, IS-127, and IS-733. Eight companies submitted candidates to selection phase. For all of the 36 test conditions, the Conexant SMV candidate was ranked at the top, or was statistically equivalent to other top-ranking candidates. The Conexant SMV candidate was chosen as the core speech coding technology for the SMV system. This paper describes the SMV algorithm developed by Conexant.

The SMV algorithm selected by TIA and 3GPP2 for CDMA applications

A technique is described for use in a decoder configured to decode a series of frames representing an encoded audio signal. The technique is for transitioning between a lost frame and one or more received frames following the lost frame in the series of frames. In accordance with the technique, an output audio signal associated with the lost frame is synthesized. An extrapolated signal is generated based on the synthesized output audio signal. A time lag is calculated between the extrapolated signal and a decoded audio signal associated with the received frame(s), wherein the time lag represents a phase difference between the extrapolated signal and the decoded audio signal. The decoded audio signal is time-warped based on the time lag, wherein time-warping the decoded audio signal comprises stretching or shrinking the decoded audio signal in the time domain.

Time-warping of decoded audio signal after packet loss

Methods, systems, and apparatuses are described for performing speaker-identification-assisted speech processing in an uplink path of a communication device. In accordance with certain embodiments, a communication device includes speaker identification (SID) logic that is configured to identify the identity of a near-end speaker. Knowledge of the identity of the near-end speaker is then used to improve the performance of one or more uplink speech processing algorithms implemented on the communication device.

Speaker-identification-assisted uplink speech processing systems and methods

This paper presents the 4 kbit/s speech coding candidate submitted by AT&T, Conexant, Deutsche Telekom, France Telecom, Matsushita, and NTT for the ITU-T 4 kbit/s selection phase. The algorithm was developed jointly based on the qualification version of Conexant. This paper focuses on the development carried out during the collaboration in order to narrow the gap to the requirements in an attempt to provide toll quality at 4 kbit/s. This objective is currently being verified in independent subjective tests coordinated by ITU-T and carried out in multiple languages. Subjective tests carried out during the development indicate that the collaboration work has been successful in improving the quality, and that meeting a majority of the requirements in the extensive selection phase test is a realistic goal.

A candidate for the ITU-T 4 kbit/s speech coding standard

Techniques are described herein that suppress noise using multiple sensors (eg, microphones) of a communication device Noise modeling (eg, estimation of noise basis vectors and noise weighting vectors) is performed with respect to a noise signal during operation of a communication device to provide a noise model The noise model includes noise basis vectors and noise coefficients that represent noise provided by audio sources other than a user of the communication device Speech modeling (eg, estimation of speech basis vectors and speech weighting) is performed to provide a speech model The speech model includes speech basis vectors and speech coefficients that represent speech of the user A noisy speech signal is processed using the noise basis vectors, the noise coefficients, the speech basis vectors, and the speech coefficients to provide a clean speech signal

Noise suppression using multiple sensors of a communication device

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