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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

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…