def main_2():
short_term_mem = []
data_cache = dict()
m = pi_audio.Microphone()
m.start_stream()
fft = pi_signal.fft_object(CHUNK_SIZE, FREQUENCY)
count = 0
data = list()
while True:
data += m.get_stream_buffer()
if len(data) >= CHUNK_SIZE:
bar = fft.get_group_note_for_data(data[0:CHUNK_SIZE])[0].getasstr()
if bar in data_cache.keys():
data_cache[bar] = [time.time(), data_cache[bar][1] + 1]
else:
data_cache[bar] = [time.time(), 1]
# FIFO buffer as short-term memory
if bar in short_term_mem:
short_term_mem.remove(bar)
short_term_mem = [bar] + short_term_mem
print "{0} @ {2} | {1} vs {3}".format(bar, count, data_cache[bar],
len(data_cache.keys()))
#time.sleep(.2)
count += 1
# Delete used data portion
data = data[CHUNK_SIZE:]
return short_term_mem, data_cache, mic
def __init__(self, fps=30, left_channel_index=2, right_channel_index=3):
#Create started variable
self._started = False
#Setup publisher - Setup pub before init !Order Matters!
self.audio_data_pub = rospy.Publisher('AudioData_01',
AudioData,
queue_size=10)
#Initialize this module as a ros node
rospy.init_node('audio_publisher', anonymous=True)
#Setup refresh rate variables
self.fps = min(max(fps, 1), 30) #constrain bwt 1 and 30
self.rate = rospy.Rate(self.fps)
#Setup Microphone Device index
self.left_device_index = left_channel_index
self.right_device_index = right_channel_index
#Create data buffer
self.audio_data_buffer = AudioData()
#Create Mic Object
self.left_microphone = pi_audio.Microphone()
def test_1():
mic = pi_audio.Microphone()
mic.start_stream()
sample_frequency = mic.rate
number_of_sample_points = mic.chunk_size
fft = pi_signal.fft_object(number_of_sample_points, sample_frequency)
def __generate_data():
data = mic.get_recent_chunked_stream_buffer()
if data:
data = fft.get_group_note_for_data(data)
return data
return None
cluster = NodeCluster()
cluster.add_parallel_node(PrimaryPitchPrinter())
cluster.add_parallel_node(NoteGroupChangeLog())
cluster.add_parallel_node(NoteFrequencyTracker())
cluster.add_parallel_node(NoteGroupOverTimeGraph())
cluster.add_parallel_node(
pi_signal.NoteClustifier(LOG_DIR + "note_clusters_over_time.log"))
task_processor = ChunkStreamSimultaneousProcessor()
task_processor.add_data_inline_process(__generate_data, cluster)
task_processor.start_processing() # blocking
task_processor.stop_processing()
def main():
print "--Recording--"
m = pi_audio.Microphone()
m.record(5)
print "-- Done --"
print "-- Playing --"
s = pi_audio.Speaker()
s.start_stream()
s.push_data(m.data.tolist())
print "-- Done --"
s.stop_stream()
def main_1():
print "Getting mic ..."
m = pi_audio.Microphone()
print "Starting stream ...",
print m.start_stream()
gdn = None
for i in xrange(3):
print "Getting stream ...",
data = m.get_stream_buffer()
print len(data)
if len(data) > 0:
fft = pi_signal.fft_object(CHUNK_SIZE, FREQUENCY)
gdn = fft.get_group_note_for_data(data)
print "Stoping stream ..."
m.stop_stream()
return m, gdn
def main_25():
MAX_MEM_WORD_LENGTH = 20
short_term_mem = []
data_cache = dict()
m = pi_audio.Microphone()
m.start_stream()
fft = pi_signal.fft_object(CHUNK_SIZE, FREQUENCY)
count = 0
data = list()
word = list()
words = list()
heard_time = list()
word_freq_count = list()
while True:
data += m.get_stream_buffer()
if len(data) >= CHUNK_SIZE:
bar = fft.get_group_note_for_data(data[0:CHUNK_SIZE])[0].getasstr()
word.append(bar)
if len(word) <= 0:
pass
elif word in words:
index = words.index(word)
heard_time[index] = time.time()
word_freq_count[index] += 1
elif not word in words:
words.append(word)
heard_time.append(time.time())
word_freq_count.append(1)
elif (len(word) >= MAX_MEM_WORD_LENGTH):
word = list()
print "word = {0}".format(word)
# Delete used data portion
data = data[CHUNK_SIZE:]
return words, heard_time, word_freq_count, mic