Industrial Technology Research Institute

Huang-Lin Yang

Hwai-Tsu Chang

Tzi-Dar Chiueh

Chung-Chih Chang

Wen-Whei Chang

De-Yu Wang

Yeh-Rong Hsu

The present invention provides a sinusoidal transform vocoder based on the Bark spectrum, which has high quality and low bit rate for coding. The present invention includes the steps of transforming a harmonic sine wave from a frequency spectrum to a perception-based Bark spectrum. An equal-loudness pre-emphasis and the loudness to a subjective loudness transformation are also involved in the method. Last, a pulse code modulation (PCM) is used to quantize the subjective loudness to obtain quantized subjective loudness. In synthesis, the Bark spectrum is inversely processed to obtain the excitation pattern following the sone-to-phone conversion and equal-loudness deemphasis. Then, the sine wave amplitudes can be estimated from the excitation pattern by assuming that the amplitudes belonging to the same critical band are equal.

Method of amplitude coding for low bit rate sinusoidal transform vocoder

A reconfigurable neural network is disclosed. The neural network includes a plurality of switches each having at least two conductive leads, wherein data flow direction of the conductive leads of the switches is programmed to select one of the conductive leads as input switch lead and select another one of the conductive leads as an output switch lead. A plurality of processing elements each having a plurality of leads connected to the switches, wherein the processing elements and the switches are interconnected in one-dimension manner. Each of the processing elements comprising: (a) a serial-in-parallel-out accumulator having a first input coupled to one of the interconnected switches and generating a first output; (b) an activation function for transforming the first output of the serial-in-parallel-out accumulator and generating a second output; and (c) a parallel-in-serial-out shift register for shifting out the second output of the activation function serially to one of the interconnected switches.

Reconfigurable neural network

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Taipei, Taiwan

Huang-Lin Yang