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Lossy Compression. Lossless Compression. Run length encoding Huffman Coding. Lossy Compression. Human Auditory System: not sensitive data can be discarded in order to compress the data. 2 Hz to 2000 Hz. Silence Compression:. Silence compression treats small samples as if they are silence.

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Lossless compression
Lossless Compression

  • Run length encoding

  • Huffman Coding


Lossy compression1
Lossy Compression

  • Human Auditory System: not sensitive data can be discarded in order to compress the data.

    • 2 Hz to 2000 Hz


Silence compression
Silence Compression:

  • Silence compression treats small samples as if they are silence.

  • This generates run-length of zeros.

  • Silence compression is a variant of RLE.

  • It is very much suitable for sound compression.


Lossy compression

ADPCM (Advanced Differential Pulse Code Modulation)

  • Adjacent Audio sample are tend to be similar in size. The simplest way to exploit this redundancy is to subtract adjacent samples and code the differences. Which tend to be smaller in size. Any audio compression based on this principle is called DPCM. Better results can be achieved by an adaptive version called ADPCM.

  • ADPCM uses the previous sample to predict the current sample. It than computes the difference and quantizes.

  • For each input sample X[n], the output C[n] of the encoder is simply a certain number of quantization level.

  • The decoder multiples this number by quantization step to obtain the reconstructed audio sample.


Adpcm encoding
ADPCM Encoding

  • The adaptive quantizer receives the difference D[n] between the current input sample X[n] and the prediction Xp[n-1] and computes and outputs the quantized code C[n].

  • The same code is sent to adaptive dequantizer which produce next dequantized difference value Dq[n].

  • This value is added to the previous predictor output Xp[n-1] and Xp[n] is sent to the predictor to be used in the next step.


Adpcm decoding
ADPCM Decoding

  • It inputs a code C[n], dequantizes it to a difference Dq[n], which is added to a preceding predictor Xp[n-1] to form the next output Xp[n].

  • The next output is also fed into a predictor, to be used in the next step.