def passiveListen(self, PERSONA):

        THRESHOLD_MULTIPLIER = 1.8
        RATE = 16000
        CHUNK = 1024

        # number of seconds to allow to establish threshold
        THRESHOLD_TIME = 1

        # number of seconds to listen before forcing restart
        LISTEN_TIME = 10

        # prepare recording stream
        stream = self._audio.open(format=pyaudio.paInt16,
                                  channels=1,
                                  rate=RATE,
                                  input=True,
                                  frames_per_buffer=CHUNK)

        # stores the audio data
        frames = []

        # stores the lastN score values
        lastN = [i for i in range(30)]

        # calculate the long run average, and thereby the proper threshold
        for i in range(0, RATE / CHUNK * THRESHOLD_TIME):

            data = stream.read(CHUNK)
            frames.append(data)

            # save this data point as a score
            lastN.pop(0)
            lastN.append(self.getScore(data))
            average = sum(lastN) / len(lastN)

        # this will be the benchmark to cause a disturbance over!
        THRESHOLD = average * THRESHOLD_MULTIPLIER

        # save some memory for sound data
        frames = []

        # flag raised when sound disturbance detected
        didDetect = False

        # start passively listening for disturbance above threshold
        for i in range(0, RATE / CHUNK * LISTEN_TIME):

            data = stream.read(CHUNK)
            frames.append(data)
            score = self.getScore(data)

            if score > THRESHOLD:
                didDetect = True
                break

        # no use continuing if no flag raised
        if not didDetect:
            print "No disturbance detected"
            stream.stop_stream()
            stream.close()
            return (None, None)

        # cutoff any recording before this disturbance was detected
        frames = frames[-20:]

        # otherwise, let's keep recording for few seconds and save the file
        DELAY_MULTIPLIER = 1
        for i in range(0, RATE / CHUNK * DELAY_MULTIPLIER):

            data = stream.read(CHUNK)
            frames.append(data)

        # save the audio data
        stream.stop_stream()
        stream.close()

        with tempfile.NamedTemporaryFile(mode='w+b') as f:
            wav_fp = wave.open(f, 'wb')
            wav_fp.setnchannels(1)
            wav_fp.setsampwidth(pyaudio.get_sample_size(pyaudio.paInt16))
            wav_fp.setframerate(RATE)
            wav_fp.writeframes(''.join(frames))
            wav_fp.close()
            f.seek(0)
            # check if PERSONA was said
            transcribed = self.passive_stt_engine.transcribe(f)

        if any(PERSONA in phrase for phrase in transcribed):
            return (THRESHOLD, PERSONA)

        return (False, transcribed)
Listens for PERSONA in everyday sound. Times out after LISTEN_TIME, so
dataset/ETHPy150Open jasperproject/jasper-client/client/mic.py/Mic.passiveListen