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

Example methods, apparatus and articles of manufacture to provide media content are disclosed. A disclosed example method includes receiving audio output by a first media presentation device, obtaining at least one of a Nielsen code or an Arbitron® code from the audio, the obtained code being representative of at least one of the first media content or a broadcaster of the first media content, obtaining second media content based on the extracted code, and presenting the second media content on a second media presentation device different from the first media presentation device.

Methods, apparatus and articles of manufacture to provide secondary content in association with primary broadcast media content

Features, including one or more acoustic features, visual features, linguistic features, and physical features may be extracted from signals obtained by one or more sensors with a processor. The acoustic, visual, linguistic, and physical features may be analyzed with one or more machine learning algorithms and an emotional state of a user may be extracted from analysis of the features. It is emphasized that this abstract is provided to comply with the rules requiring an abstract that will allow a searcher or other reader to quickly ascertain the subject matter of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.

Multi-modal sensor based emotion recognition and emotional interface

The described embodiments include a system for communicating between electronic devices. During operation, a receiving electronic device receives a data channel protocol data unit (PDU) in a link layer of a Bluetooth Low Energy (BTLE) protocol stack. The receiving electronic device then reads a field in a header of the data channel PDU to determine if the header indicates that a payload of the data channel PDU contains audio data. When the header indicates that the payload of the data channel PDU contains audio data, the receiving electronic device is configured to send the audio data from the payload to an audio layer in the BTLE protocol stack for processing.

Audio transfer using the bluetooth low energy standard

In this paper, we propose the regression-based packet loss concealment (PLC) for digital speech transmission by using deep neural networks (DNNs) with a multiple-layer deep architecture. For the DNN training, log-power spectra and phases are employed as features in the input layer for the large training set, which ensures non-linear mapping the frames from the last correctly received frame to the missing frame. Once the training is accomplished by the restricted Boltzmann machine (RBM)-based pre-training to initialize the DNN, minimum mean square error (MMSE)-based fine tuning is then performed based on the back-propagation algorithm. In the reconstruction stage, the trained DNN model is fed with the features of the previous frames in order to estimate the log-power spectra and phases of the missing frames. Reconstruction is further improved by using the cross-fading technique to mitigate discontinuity between the reconstruction signal and good frame signal in the time-domain. To demonstrate the performance of the proposed algorithm, hidden Markov model (HMM)-based PLC algorithm and the PLC algorithm standardized in adaptive multi-rate wideband (AMR-WB) Appendix I were used for comparison. The experimental results show that the proposed approach provides better speech quality and speech recognition accuracy than the conventional approaches.

Packet loss concealment based on deep neural networks for digital speech transmission

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

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

A method and system for refining an estimated pitch period estimate based on a coarse pitch useful for performing frame loss concealment in an audio decoder as well as for other applications. A normalized correlation at the coarse pitch lag is computed and used as the current best candidate. The normalized correlation is then evaluated at the midpoint of the refinement pitch range on either side of the current best candidate. If the normalized correlation at either midpoint is greater than the current best lag, the midpoint with the maximum correlation is selected as the current best lag. After each iteration, the refinement range is decreased by a factor of two and centered on the current best lag. This bisectional search continues until the pitch has been refined to an acceptable tolerance or until the refinement range has been exhausted. During each step of the bisectional pitch refinement, the signal is decimated to reduce the complexity of computing the normalized correlation.

Decimated bisectional pitch refinement

This paper presents a candidate for the ITU-T G.722 packet loss concealment standard. The algorithm is based on waveform extrapolation in the speech domain. The strong backward adaptive nature of G.722 makes the state update during lost frames a challenge. The paper presents methods to update the G.722 subband decoder state memory during packet loss. Furthermore, novel techniques to facilitate smooth transition after packet loss are described. Formal subjective test results indicate that the algorithm far exceeds all requirements in the ITU-T terms of reference. Most techniques are applicable to G.726 as well, and some are easily extendable to other speech coders with memory.

A Candidate for the ITU-T G.722 Packet Loss Concealment Standard

Packet loss concealment (PLC) systems and methods are described that use time-warping to merge a concealment signal generated to replace one or more bad frames of an audio signal with a received signal representing one or more subsequent good frames of the audio signal in a manner that avoids signal discontinuity and audible artifacts resulting therefrom. Prediction-based PLC systems and methods are also described that use time-warping to conceal the loss of one or more frames containing a transition region in a manner that will not result in an audible artifact.

Time-warping of audio signals for packet loss concealment

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