Description
1. This assignment will make use of the “Music & Speech” dataset of Marsyas:
• You can download the dataset from: http://opihi.cs.uvic.ca/sound/music_
speech.tar.gz
• This dataset has two copies of each song, delete the music/ and speech/ directories
and use the files in music-wav/ and speech-wav/ directories. There are 64 music
and 64 speech files. Each file has 30 seconds of audio stored as 16-bit signed integers
at 22050 Hz.
• Ground truth data for this dataset can be downloaded from IVLE. Format of the
file is filename \t (tab) label \n (newline), one song per line:
filename1\tlabel1\n
filename2\tlabel2\n
…
filename128\tlabel128\n
The label field is either music or speech.
2. Follow the following steps to complete this assignment:
• Read the ground-truth file (music speech.mf).
• Load each wav file and convert the data to floats by dividing the samples by 32768.0.
Hint: use scipy.io.wavfile.read()
• Split the data into buffers of length 1024 with 50% overlap (or a hopsize of 512).
Only keep complete buffers, e.g. if the last buffer only has 1020 samples, omit it.
Hint: the starting and ending indices for the first few buffers are:
Buffer number start index end index
(not included in array)
0 0 1024
1 512 1536
2 1024 2048
. . .
We recommend that you use the “array slicing” feature provided by numpy:
for i in range(num_buffers):
start = …
end = …
buffer_data = whole_file_data[start:end]
• For each file, calculate time domain features for each buffer according to the given
formula. Given X = {x0, x1, x2, . . . xN−1} (N = 1024 for this assignment):
(a) Root-mean-squared (RMS):
XRMS =
vuut
1
N
N
X−1
i=0
x
2
i
1
(b) Zero crossings (ZCR):
XZCR =
1
N − 1
N
X−1
i=1 (
1 if (xi
· xi−1) < 0
0 else
• After calculating the features for each buffer, calculate the mean and uncorrected
sample standard deviation for each feature over all buffers for each file.
• Now you have finished calculating time domain features. To calculate frequency
domain features, multiply each buffer with a Hamming window.
Hint: use scipy.signal.windows.hamming()
• Perform a Discrete Fourier Transform for each windowed buffer.
Hint: use scipy.fft().
Note: the DFT gives you both “positive” and “negative” frequencies, whose values
are mirrored around the Nyquist frequency. Discard the negative frequencies (whose
array indices are above N/2 for an FFT of length N).
• Calculate the following frequency domain features for each spectral buffer. Given a
spectral buffer X:
(a) Spectral Centroid (SC):
SC =
PN−1
k=0 k · |X[k]|
PN−1
k=0 |X[k]|
(b) Spectral Roll-Off (SRO): which is the smallest bin index R such that L energy
is below it. For this assignment, we will use L = 0.85.
X
R−1
k=0
|X[k]| ≥ L ·
N
X−1
k=0
|X[k]|
(c) Spectral Flatness Measure (SFM):
SFM =
exp
1
N
PN−1
k=0 ln |X[k]|
1
N
PN−1
k=0 |X[k]|
Note: using the log-scale is useful for avoiding multiplications which may exceed
the bounds of double floating-point arithmetic.
• After calculating the features for each buffer, calculate the mean and uncorrected
sample standard deviation for each feature over all buffers for each file.
• Output your results to a new ARFF file and name it as results.arff. The header
of it should be like:
@RELATION music_speech
@ATTRIBUTE RMS_MEAN NUMERIC
@ATTRIBUTE ZCR_MEAN NUMERIC
@ATTRIBUTE SC_MEAN NUMERIC
@ATTRIBUTE SRO_MEAN NUMERIC
@ATTRIBUTE SFM_MEAN NUMERIC
@ATTRIBUTE RMS_STD NUMERIC
@ATTRIBUTE ZCR_STD NUMERIC
@ATTRIBUTE SC_STD NUMERIC
@ATTRIBUTE SRO_STD NUMERIC
@ATTRIBUTE SFM_STD NUMERIC
@ATTRIBUTE class {music,speech}
The format of the data section should be:
@DATA
RMS_MEAN1,ZCR_MEAN1,SC_MEAN1,SRO_MEAN1,SFM_MEAN1,RMS_STD1,ZCR_STD1,SC_STD1,SRO_STD1,SFM_STD1,music
…
Concretely, the @DATA section should be:
2
@DATA
0.057447,0.191595,128.656296,239.404651,0.329993,0.027113,0.036597,13.206525,27.957121,0.087828,music
…
0.062831,0.082504,78.481380,145.886047,0.198849,0.032323,0.070962,39.388633,66.942115,0.133545,speech
Note: Please keep at least 6 digits after the decimal point for output.
3. Submit a zip file to IVLE containing your source code (a single .py file) and the ARFF
file. Name the zip file using your student number (e.g. A0123456H.zip).
4. Note: You may use any python standard libraries, numpy (including pylab / matplotlib)
and scipy. No other libraries are permitted. Late submissions will receive no marks.
5. Grading scheme:
• 4/6 marks: correct ARFF file.
• 2/6 marks: readable source code (good variable names, clean functions, necessary
comments).
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