What is the Auditory Toolbox?

This version of the toolbox fixes several bugs, especially in the Gammatone and MFCC implementations, and adds several new functions. This report was previously published as Apple Computer Technical Report #45. We appreciate receiving permission from Apple Computer to republish their code and to update this package.

There are many ways to describe and represent sounds. The figure below shows one taxonomy based on signal dimensionality. A simple waveform is a one-dimensional representation of sound. The two-dimensional representation describes the acoustic signal as a time-frequency image. This is the typical approach for sound and speech analysis. This toolbox includes conventional tools such as the short-time-Fourier-Transform (STFT or Spectrogram) and several cochlear models that estimate auditory nerve firing ãprobabilitiesä as a function of time. Finally, the next level of abstraction is to summarize the periodicities of the cochlear output with the correlogram. The correlogram provides a powerful representation that makes it easier to understand multiple sounds and to perform auditory scene analysis.
 

What does the Auditory Toolbox contain?

1.  Richard F. Lyon has described an auditory model based on a transmission line model of the basilar membrane and followed by several stages of adaptation. This model can represent sound at either a fine time scale (probabilities of an auditory nerve firing) or at the longer time scales characteristic of the spectrogram or MFCC analysis. The LyonPassiveEar command implements this particular ear model.
2.  Roy Patterson has proposed a model of psychoacoustic filtering based on critical bands. This auditory front-end combines a Gammatone filter bank with a model of hair cell dynamics proposed by Ray Meddis. This auditory model is implemented using the MakeERBFilters, ERBFilterBank, and MeddisHairCell commands.
3. Stephanie Seneff has described a cochlear model that combines a critical band filterbank with models of detection and automatic gain control. This toolbox implements stages I and II of her model.
4. Conventional FFT analysis is represented using the spectrogram. Both narrow band and wide band spectrograms are possible. See the spectrogram command for more information.
5. A common front-end for many speech recognition systems consists of Mel-frequency cepstral coefficients (MFCC). This technique combines an auditory filter-bank with a cosine transform to give a rate representation roughly similar to the auditory system. See the mfcc command for more information. In addition, a common technique known as rasta is included to filter the coefficients, simulating the effects of masking and providing speech recognition system a measure of environmental adaptation.
6. Conventional speech-recognition systems often use linear-predictive analysis to model a speech signal. The forward transform, proclpc, and its inverse, synlpc are included.

How do I get the Auditory Toolbox?

• Documentation (PDF, 849k)
• Unix archive, includes documentation (TAR, 1.5M)
• Macintosh archive, includes documentation  (self extracting Bin Hex, 1.4M)
• Windows archive, includes documentation  (WinZip, 0.9M)

These archives contain ".m" files, MATLAB mex files, and the C sources needed to create the mex files. I have tested this code on Macintosh, PC, SGI, and Sun computers running MATLAB 5.2. The code is reasonably portable, so I don't expect any problems on any machine running MATLAB.

After installing this software on your machine, use the test_auditory script to run through the examples in the documentation.
 

Is there support for the Auditory Toolbox?

Please let me know if you have comments or questions. I can be reached at