Linear predictive coding

About: Linear predictive coding is a research topic. Over the lifetime, 6565 publications have been published within this topic receiving 142991 citations. The topic is also known as: Linear predictive coding, LPC.

Drone Detection using Audio Analysis

Abstract: Drones used for illegal purposes is a growing problem and a way to detect these is needed. This thesis has evaluated the possibility of using sound analysis as the detection mechanism. A solution using linear predictive coding, the slope of the frequency spectrum and the zero crossing rate was evaluated. The results showed that a solution using linear predictive coding and the slope of the frequency spectrum give a good result for the distance it is calibrated for. The zero crossing rate on the other hand does not improve the result and was not part of the final solution. The amount of false positives increases when calibrating for longer distances, and a compromise between detecting drones at long distances and the number of false positives need to be made in the implemented solution. It was concluded that drone detection using audio analysis is possible, and that the implemented solution, with linear predictive coding and slope of the frequency spectrum, could with further improvements become a useable product.

Rate selection method for selectable mode vocoder

Abstract: There is provided rate selection methods and systems for selecting coding rates for coding frames of a speech signal to realize an average bit rate indicated by a mode. For example, a mode 0, mode 1, and a mode 2 may be defined, with each mode requiring a different average bit rate. To achieve the average bit rate of a particular mode, a coding rate is selected for each frame of the speech signal, based on the characteristics of a frame. A frame can be categorized in a class, such as noise or silence, noise-like unvoiced speech, pulse-like unvoiced speech, transition into voiced speech, unstable voiced speech, stable voiced speech. Other parameters may also be used, such as the sharpness, noise-to-signal ratio, pitch correlation, energy, and reflection coefficient. A frame may then be coded at a full-rate, a half-rate, a quarter-rate, or an eighth-rate.

Apparatus for expanding speech bandwidth

Abstract: Apparatus for expanding the bandwidth of speech signals such that a narrowband speech signal is input and digitized (101), the spectral envelope information and residual information are extracted from the digitized signal by linear predictive coding analysis (107), the spectral envelope information is expanded into wideband information by a spectral envelope converter (109), the residual information is expanded into wideband information by a residual converter (110), the converted spectral envelope information and residual information are combined (108) to produce a wideband speech signal, frequency information not contained in the input signal is extracted from the obtained wideband speech signal by a filter (105), and the resulting signal is added (103) to the original digitized input signal, and the obtained signal is converted (104) into an analog signal as the output signal of the apparatus.

Unsupervised Learning of Overlapped Speech Model Parameters For Multichannel Speech Activity Detection in Meetings

Abstract: The study of meetings, and multi-party conversation in general, is currently the focus of much attention, calling for more robust and more accurate speech activity detection systems. We present a novel multichannel speech activity detection algorithm, which explicitly models the overlap incurred by participants taking turns at speaking. Parameters for overlapped speech states are estimated during decoding by using and combining knowledge from other observed states in the same meeting, in an unsupervised manner. We demonstrate on the NIST Rich Transcription Spring 2004 data set that the new system almost halves the number of frames missed by a competitive algorithm within regions of overlapped speech. The overall speech detection error on unseen data is reduced by 36% relative.

Codebook-based Bayesian speech enhancement

Abstract: In this paper, we propose a Bayesian approach for the estimation of the short-term predictor parameters of speech and noise, from the noisy observation. The resulting estimates of the speech and noise spectra can be used in a Wiener filter or any state-of-the-art speech enhancement system. We utilize a-priori information about both speech and noise in the form of trained codebooks of linear predictive coefficients. In contrast to current Bayesian estimation approaches that consider the excitation variances as part of the a-priori information, in the proposed method they are computed analytically based on the observation at hand. Consequently, the method performs well in nonstationary noise conditions. Experimental results confirm the superior performance of the proposed method compared to existing Bayesian approaches, such as those based on hidden Markov models.