def relParToWave(x_rel): global infant x_abs = get_abs_coord(x_rel) if infant: wavFile = parToWave(x_abs, speaker='infant', simulation_name='infant', pitch_var=0.0, len_var=1.0, verbose=False, rank=1, different_folder='aux_infant.wav', monotone=False) else: wavFile = parToWave(x_abs, speaker='adult', simulation_name='adult', pitch_var=0.0, len_var=1.0, verbose=False, rank=1, different_folder='aux_adult.wav', monotone=False) sound = Sound(wavFile) sound = correct_initial(sound) # call correct_initial to remove initial burst sound_resampled = get_resampled(sound) # call get_resampled to adapt generated sound to AN model sound_extended = get_extended(sound_resampled) # call get_extended to equalize duration of all sounds sound_extended.save(wavFile) return sound_extended
def evaluate_environment(params, i_global, simulation_name, outputfolder, i_target=0, rank=1, speaker='adult', n_vow=5, normalize=False): folder = outputfolder ############### Sound generation if output: print 'simulating vocal tract' wavFile = parToWave(params, speaker, simulation_name, verbose=output, rank=rank) # call parToWave to generate sound file # wavFile = par_to_wav(params, speaker, simulation_name, verbose=output, rank=rank) # call parToWave to generate sound file if output: print 'wav file '+str(wavFile)+' produced' sound = loadsound(wavFile) # load sound file for brian.hears processing if output: print 'sound loaded' ############### Audio processing sound = correct_initial(sound) # call correct_initial to remove initial burst sound_resampled = get_resampled(sound) # call get_resampled to adapt generated sound to AN model sound_extended = get_extended(sound_resampled) # call get_extended to equalize duration of all sounds sound_extended.save(wavFile) # save current sound as sound file os.system('cp '+wavFile+' '+folder+'data/vowel_'+str(i_target)+'_'+str(rank)+'.wav') if playback: print 'playing back...' sound_extended.play(sleep=True) # play back sound file if output: print 'sound acquired, preparing auditory processing' out = drnl(sound_extended) # call drnl to get cochlear activation ############### Classifier evaluation flow_name = 'data/current_auditory_system.flow' flow_file = open(flow_name, 'r') # open classifier file flow = cPickle.load(flow_file) # load classifier flow_file.close() # close classifier file sample_vote_unnormalized = flow(out) # evaluate trained output units' responses for current item if normalize: sample_vote = normalize_activity(sample_vote_unnormalized) else: sample_vote = sample_vote_unnormalized mean_sample_vote = np.mean(sample_vote, axis=0) # average each output neurons' response over time confidences = get_confidences(mean_sample_vote) plot_reservoir_states(flow, sample_vote, i_target, folder, n_vow, rank) return confidences
def evaluate_environment(params, i_global, simulation_name, outputfolder, i_target=0, rank=1, speaker='adult', n_vow=5, normalize=False): folder = outputfolder ############### Sound generation if output: print 'simulating vocal tract' wavFile = parToWave(params, speaker, simulation_name, verbose=output, rank=rank) # call parToWave to generate sound file # wavFile = par_to_wav(params, speaker, simulation_name, verbose=output, rank=rank) # call parToWave to generate sound file if output: print 'wav file ' + str(wavFile) + ' produced' sound = loadsound(wavFile) # load sound file for brian.hears processing if output: print 'sound loaded' ############### Audio processing sound = correct_initial( sound) # call correct_initial to remove initial burst sound_resampled = get_resampled(sound) # call get_resampled to adapt generated sound to AN model sound_extended = get_extended(sound_resampled) # call get_extended to equalize duration of all sounds sound_extended.save(wavFile) # save current sound as sound file os.system('cp ' + wavFile + ' ' + folder + 'data/vowel_' + str(i_target) + '_' + str(rank) + '.wav') if playback: print 'playing back...' sound_extended.play(sleep=True) # play back sound file if output: print 'sound acquired, preparing auditory processing' out = drnl(sound_extended) # call drnl to get cochlear activation ############### Classifier evaluation flow_name = 'data/current_auditory_system.flow' flow_file = open(flow_name, 'r') # open classifier file flow = cPickle.load(flow_file) # load classifier flow_file.close() # close classifier file sample_vote_unnormalized = flow( out) # evaluate trained output units' responses for current item if normalize: sample_vote = normalize_activity(sample_vote_unnormalized) else: sample_vote = sample_vote_unnormalized mean_sample_vote = np.mean(sample_vote, axis=0) # average each output neurons' response over time confidences = get_confidences(mean_sample_vote) plot_reservoir_states(flow, sample_vote, i_target, folder, n_vow, rank) return confidences