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AUDIOFILES. Harika Basana ([email protected] ), Elizabeth Chan ([email protected] ), Nikolai Sinkov([email protected] ), Frank Zhang ([email protected] ) 6100 Main Street, Rice University, Houston, Texas 77005. GOAL To explore the MP3 technology and to implement various audio data compression

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Audiofiles

AUDIOFILES

Harika Basana ([email protected] ), Elizabeth Chan ([email protected] ), Nikolai Sinkov([email protected] ), Frank Zhang ([email protected] ) 6100 Main Street, Rice University, Houston, Texas 77005

  • GOAL

  • To explore the MP3 technology and

  • to implement various audio data compression

  • algorithms.

  • Analyze This

  • Audio compression is to compress an audio

    file into a smaller-sized file.

  • People cannot differentiate between these

    two files by just hearing.

  • Due to its smaller size, the new file can be easily

    transferred via the Internet.

  • People try to find better audio compression

    algorithms that retain satisfying audio quality.

Ding.wav before compression

Ding.wav with frequencies within 1 std from the mean

Original signal sampled at 44100Hz

The x-axis DT sample and the y-axis is the amplitude

After linear quantization

Ding.wav with frequencies within 2 std from the mean

Ding.wav with frequencies within 3 std from the mean

After tangent quantization

After arctangent quantization

  • Masking Algorithm

  • The presence of a signal at a particular frequency can

  • raise the perceptual threshold of signals close to the

  • the masking frequency.

    Procedure

  • Go through every sample and remove the following

    samples if they are below a certain threshold.

    Results

  • No significant improvement. Need a better way of

    implementing to get good results.

  • Algorithms

  • Average Energy Algorithm

  • Zeroes out selected high and low frequencies of

  • the audio file.

    Procedure

  • Perform the Discrete Cosine Transform (DCT).

  • Calculate the signal’s energy.

  • Find the mean and the standard deviation of

    from the energy spectrum.

  • Keep all frequencies with energies within 1

    standard deviation (std) from the mean.

  • Zero out frequencies with energies outside this range.

  • Similarly, keep frequencies with energies within 2

    and 3 stds from the mean.

  • Perform the Inverse DCT and get the output.

    Results

  • Amount of compression is insignificant.

  • Algorithm would probably work better if the signal

    is very short, has monotonous tones, and has little

    noise.

  • Psycho Acoustic Algorithm

  • Linear, tangent or arctangent quantization of the

  • signal.

    Procedure

  • Perform the Discrete Cosine Transform (DCT)

  • Quantize the signal in one of the following ways :

    Diagram of the quantization “buckets” for the three

    methods

  • Give certain frequency bands more bits

    (1000 – 5100 Hz and 12500 - 15200Hz).

  • Throw away frequencies below 20Hz and above

    20,000Hz.

  • Perform the Inverse DCT.

    Results

  • Compression is very significant.

  • Quality is good for the amount of compression.

  • Arctangent quantization yields the best quality.

  • Conclusion

  • We didn’t create MP3 files.

  • Used the underlying concepts.

  • Produced much smaller files.

  • Psycho Acoustic Algorithm is the best, in terms of

    - amount of compression

    - sound quality of the output.

  • Improvements

  • Implement windowing

  • Implement temporal masking

    Bibliography:

    http://www.sospubs.co.uk/sos/may00/articles/mp3.htm

    http://www.besar.dcs.gla.ac.uk/labs/audiolab/real_site/

    tutorials/mp3/mp3how.php and more…


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