Heart Murmur Detection Algorithm based on Spectral Flatness

Voice Activity Detection Based on Discriminative Weight Training Incorporating a Spectral Flatness Measure

In this paper, we present an approach to incorporate discriminative weight training into a statistical model-based voice activity detection (VAD) method. In our approach, the VAD decision rule is derived from the optimally weighted likelihood ratios (LRs) using a minimum classification error (MCE) method. An adaptive online means of selecting two kinds of weights based on a power spectral flatn...

Efficient voice activity detection algorithm using long-term spectral flatness measure

This paper proposes a novel and robust voice activity detection (VAD) algorithm utilizing long-term spectral flatness measure (LSFM) which is capable of working at 10 dB and lower signal-to-noise ratios(SNRs). This new LSFM-based VAD improves speech detection robustness in various noisy environments by employing a low-variance spectrum estimate and an adaptive threshold. The discriminative powe...

Erratum to: Efficient voice activity detection algorithm using long-term spectral flatness measure

Heart Murmur Recognition Based on Hidden Markov Model

Heart murmur recognition and classification play an important role in the auscultative diagnosis. The method based on hidden markov model (HMM) was presented to recognize the heart murmur. The murmur was isolated on basis of the principle of wavelet analysis considering the time-frequency characteristics of the heart murmur. This method uses Mel frequency cepstral coefficient (MFCC) to extract ...

Maximum flatness spectral modeling

The maximum entropy method obtains the flattest spectrum consistent with the given autocorrelation values, according to a specific flatness measure. In this correspondence, the suitability of the maximum flatness criterion for spectral estimation is discussed, concluding that it offers useful insight into the spectral models arising from the optimization approach.