High quality split band LPC vocoder operating at low bit rates

Atkinson, Ian A.; Yeldner, S.; Kondoz, A.M.

doi:10.1109/icassp.1997.596249

Cited by 14 publications

(2 citation statements)

References 4 publications

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“…Spectral analysis-by-synthesis methods like, e.g., [4,5,6], are based on the goodness-of-fit of the short-time speech spectrum to a harmonic sinusoidal representation. Those methods usually do not account for the distribution of the harmonic and noise energy along the frequency axis.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Voicing Cut-Off Frequency Contour Based on a Cumulative Harmonicity Score

Hermus

hamme

Irhimeh

2007

IEEE Signal Process. Lett.

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We present a new algorithm for the automatic estimation of the voicing cut-off frequency (VCO), i.e., the frequency that separates the periodic low-frequency part from the aperiodic high-frequency part in voiced segments of natural speech. Starting from the power spectrum of a two pitch period speech frame, we define the VCO to be located at the frequency for which the sum of the periodic and aperiodic energy in the spectral band below and above that frequency respectively, is maximised. By formulating the problem in terms of a score function we are able to apply a dynamic programming based smoothing technique. Remarkably smooth and accurate VCO contours were obtained, despite the simplicity of the proposed algorithm. In a formal evaluation the algorithm compares favourably to two existing VCO estimation techniques.

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Section: Introductionmentioning

confidence: 99%

Estimation of the Voicing Cut-Off Frequency Contour Based on a Cumulative Harmonicity Score

Hermus

hamme

Irhimeh

2007

IEEE Signal Process. Lett.

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“…The Short Time FFT output of a frame from the MATLAB Simulink is shown below- Pitch Analysis is performed in the spectral domain using the algorithm described by McAulay [10] which determines the pitch period to half sample accuracy. This algorithm estimates the pitch of a speech waveform that fits the harmonic set of sine waves to the input data using a mean squared error.…”

Section: Fig 22(a) Frequency Spectrum Of Kaiser Windowmentioning

confidence: 99%

Multimedia Systems Education Innovations I: Speech

Ogunfunmi¹

2012 ASEE Annual Conference &Amp; Exposition Proceedings

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Multimedia Systems is becoming very important in undergraduate education. The word multimedia refers to speech, audio and video data. Speech, audio and video and general digital signal processing (DSP) devices are very common-place in everyday life. This is due to the growth of popularity of personal digital assistants (PDA), cellular phones, and other embedded speech/audio/video devices. One of the major applications of DSP processors is in speech, audio and video processing.We are developing a 3-part course sequence that will help teach undergraduates multimedia systems. The first part of the 3-part course sequence is about speech. The second and third parts will focus on audio and video. The goal is to demystify these applications of real-time multimedia signal processing so that undergraduates can have a mastery of the basic techniques.In this paper, we provide details of a course we designed for undergraduates which focuses on understanding how to process speech. We provide examples of the curriculum, what is covered and how we cover it. We also provide examples of laboratory projects that are used to complement the class lecture sessions. We use MATLAB software in all the lab projects.We also discuss possible implementations of the speech coding and processing using hardware such as DSPs. In the future, we plan to introduce the use of FPGAs for this application as well.Details of the course and our experiences in developing and offering them will be presented at the conference.

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