def extract(filtered_audio, Fs): nfft = 256 num_bins = 40 start_frequency = 150 end_frequency = 3200 c = filtered_audio.shape[0] features = [] for i in range(c): framed_audio = sigproc.framesig(filtered_audio[i], 256, 128) features.append([]) j = framed_audio.shape[0] if j > 10: req_frames = framed_audio[int(j / 2) - 5:int(j / 2) + 5] print(req_frames) for k in range(len(req_frames)): peak_amp, peak_freq = sp_peak_amp_freq.peakFreq(req_frames[k], 50) pitch_periods = sp_pitch_period.pitch_period(req_frames[k], Fs) form = formants.formant(req_frames[k]) cep = LPCC.lpcc(req_frames[k]) real_cc = RCC.rcc(req_frames[k]) lsfs = lsf.LSF(req_frames[k]) hjorth_parameters = hjorth.params(req_frames[k]) wavelet = dwt.wenergy(req_frames[k], 'db7', 5) features[i].extend(lsfs) features[i].extend(hjorth_parameters) features[i].extend(wavelet) return features
def create_from_options(cls, parser, options): noptions = ((options.ipython_profile and 1 or 0) + (options.lsf_directory and 1 or 0) + (options.multiprocessing and 1 or 0)) if noptions > 1: parser.error('You can only specify one of --ipython-profile, --lsf-directory, and --multiprocessing.') if options.lsf_directory: import lsf return lsf.LSF(options.njobs, options.lsf_directory, memory=options.memory, job_array_name = options.jobname) elif options.ipython_profile: from IPython.parallel import Client, LoadBalancedView client = Client(profile=options.ipython_profile) return IPython(client) elif options.multiprocessing: return Multiprocessing() else: return Uniprocessing()
def extract(filtered_audio, Fs): nfft = 256 num_bins = 40 start_frequency = 150 end_frequency = 3200 c = filtered_audio.shape[0] features = [] for i in range(c): features.append([]) peak_amp, peak_freq = sp_peak_amp_freq.peakFreq(filtered_audio[i], 50) pitch_periods = sp_pitch_period.pitch_period(filtered_audio[i], Fs) form = formants.formant(filtered_audio[i]) cep = LPCC.lpcc(filtered_audio[i]) real_cc = RCC.rcc(filtered_audio[i]) lsfs = lsf.LSF(filtered_audio[i]) hjorth_parameters = hjorth.params(filtered_audio[i]) wavelet = dwt.wenergy(filtered_audio[i], 'db7', 5) features[i].extend(lsfs) features[i].extend(hjorth_parameters) features[i].extend(wavelet) return features