scl = scale(args.fmin, args.fmax, args.bins, beyond=int(args.reducedform == 2))
slicq = NSGT_sliced(scl,
args.sllen,
args.trlen,
fs,
real=args.real,
recwnd=args.recwnd,
matrixform=args.matrixform,
reducedform=args.reducedform,
multithreading=args.multithreading,
multichannel=True)
# Read audio data
sf = SndReader(args.input, sr=fs, chns=2)
signal = sf()
# duration of signal in s
dur = sf.frames / float(fs)
# total number of coefficients to represent input signal
ncoefs = int(sf.frames * slicq.coef_factor)
# read slices from audio file and mix down signal, if necessary at all
if not args.downmix_after:
signal = ((np.mean(s, axis=0), ) for s in signal)
# generator for forward transformation
c = slicq.forward(signal)
default=4096,
help="STFT window size")
parser.add_argument("--overlap", type=int, default=1024, help="STFT overlap")
parser.add_argument("--plot",
action='store_true',
help="Plot transform (needs installed matplotlib package)")
parser.add_argument("--mono", action='store_true', help="Audio is mono")
args = parser.parse_args()
if not os.path.exists(args.input):
parser.error("Input file '%s' not found" % args.input)
fs = args.sr
# Read audio data
sf = SndReader(args.input, sr=fs, chns=2, blksz=2**32)
signal = sf()
signal = np.asarray(list(signal))
signal = signal.T
signal = np.squeeze(signal, axis=-1)
signal = np.mean(signal, axis=-1)
# duration of signal in s
dur = sf.frames / float(fs)
print(f'signal: {signal.shape}, dur: {dur}, frames: {sf.frames}')
if args.plot:
fig = plt.figure()
plt.rcParams.update({'font.size': args.fontsize})