3G Mobile Open Wide Door For E-commerce
The 3G mobile Internet business in 3G era will obtain rapid development, but it still
cannot become the ma in 3G era. In the age of 3G Speech business, but is still a subject
of value-added business will have great development, the 3G mobile Internet business.
Java Media APIs: Cross-Platform Imaging, Media, and Visualization presents integrated Java media solutions that demonstrate the best practices for using this diverse collection. According to Sun MicroSystems, "This set of APIs supports the integration of audio and video clips, animated presentations, 2D fonts, graphics, and images, as well as Speech input/output and 3D models." By presenting each API in the context of its appropriate use within an integrated media application, the authors both illustrate the potential of the APIs and offer the architectural guidance necessary to build compelling programs.
Face Transfer is a method for mapping videorecorded perfor-mances of one individual to facial animations of another. It extracts visemes (Speech-related mouth articulations), expressions, and three-dimensional (3D) pose from monocular video or 鏗乴m footage.
The philosophy of the book is to present various pattern recognition tasks in
a unified way, including image analysis, Speech processing, and communication applications. Despite their differences, these areas do share common features and their study can only benefit from a unified approach.
C Algorithms for Real-Time DSP
Chapter 5 presents several real-time DSP applications, including Speech compression music signal processing radar signal processing and adaptive signal processing techniques.
this file has codes that describes how to ccmpute the signal spectrum , the power spectrum, how to calculate the autocorrelation sequence of a signal, how to calculate the autoregressive coeffecients of a signal,and how to reduce the noisy elements in a Speech sample.
Commercially available active noise control headphones rely on fixed analog controllers to drive "anti-noise" loudspeakers. Our design uses an adaptive controller to optimally cancel unwanted acoustic noise. This headphone would be particularly useful for workers who operate or work near heavy machinery and engines because the noise is selectively eliminated. Desired sounds, such as Speech and warning signals, are left to be heard clearly. The adaptive control algorithm is implemented on a Texas Instruments (TI™ )
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TMS320C30GEL digital signal processor (DSP), which drives a Sony CD550 headphone/microphone system. Our experiments indicate that adaptive noise control results in a dramatic improvement in performance over fixed noise control. This improvement is due to the availability of high-performance programmable DSPs and the self-optimizing and tracking
capabilities of the adaptive controller in response to the surrounding noise.
This report presents a tutorial of fundamental array processing and beamforming theory relevant to microphone array Speech processing. A microphone array consists of multiple microphones placed at different spatial locations. Built upon a knowledge of sound propagation principles, the multiple inputs can be manipulated to enhance or attenuate signals emanating from particular directions. In this way, microphone arrays provide a means of enhancing a desired signal in the presence of corrupting noise sources. Moreover, this enhancement is based purely on knowledge of the source location, and so microphone array techniques are applicable to a wide variety of noise types. Microphone arrays have great potential in practical applications of Speech processing, due to their ability to provide both noise robustness and hands-free signal acquisition.
The 4.0 kbit/s Speech codec described in this paper is based on a
Frequency Domain Interpolative (FDI) coding technique, which
belongs to the class of prototype waveform Interpolation (PWI)
coding techniques. The codec also has an integrated voice
activity detector (VAD) and a noise reduction capability. The
input signal is subjected to LPC analysis and the prediction
residual is separated into a slowly evolving waveform (SEW) and
a rapidly evolving waveform (REW) components. The SEW
magnitude component is quantized using a hierarchical
predictive vector quantization approach. The REW magnitude is
quantized using a gain and a sub-band based shape. SEW and
REW phases are derived at the decoder using a phase model,
based on a transmitted measure of voice periodicity. The spectral
(LSP) parameters are quantized using a combination of scalar
and vector quantizers. The 4.0 kbits/s coder has an algorithmic
delay of 60 ms and an estimated floating point complexity of
21.5 MIPS. The performance of this coder has been evaluated
using in-house MOS tests under various conditions such as
background noise. channel errors, self-tandem. and DTX mode
of operation, and has been shown to be statistically equivalent to
ITU-T (3.729 8 kbps codec across all conditions tested.
