Bitzer J, Kissner S, Holube I (2016). Privacy-Aware Acoustic Assessments of Everyday Life. Journal of the Audio Engineering Society, 64(6), 395-404. Download the OpenAccess paper from the Journal of the Audio Engineering Society.
Please clone the Git repository. In order to start with the framework, go to
matlab/Paper/ and read
The code to produce Figures 3 to 7 (system evaluation) can be found in
Smartphone feature extraction
java/ contains the feature extraction algorithms used on the Android system. See Sec. 2 of the paper for details.
As stated in the paper far more features have been computed to compare the smartphone based feature extraction to the conventional audio based extraction methods. Here are the final results:
The delta derivates of the MFCC coefficients
The standard deviation of the MFCC coefficients
The centroid results
The power spectrum entropy (PSE) results
The Broadband Envelope (Frequency Domain) (BEF) results
The Broadband Envelope Correlation and Lag (Frequency Domain) results
The Broadband Envelope Correlation and Lag (Time Domain, RMS-based) results
Fig. 4 in the article shows the noise level exhibited by the microphones as a function of frequency. The ordinate is specified as Noise Level in dB SPL. This was calculated by appying the broadband calibration to every bin of the power spectral density of the measured noise. This is wrong for two reasons:
- The power spectral density has not been properly properly scaled.
- The sound pressure for a single frequency bin is in no way a useful measure.
(Note that relative levels shown in the published figure are still valid.)
The figures below show the noise level in dB SPL, properly scaled, in (third-)octaves. The code for producing these figures can be found in