使用dejavu(Python脚本)麦克风歌曲识别和从智能手机(phonegap)到服务器的音频数据流

时间:2019-01-07 14:22:49

标签: python-2.7 cordova audio-streaming phonegap audio-fingerprinting

我正在尝试创建一个带有phonegap的应用程序,以识别诸如shazam之类的歌曲。我使用的是很棒的python脚本dejavu,但是当我尝试识别使用智能手机录制的歌曲样本时遇到了问题。当从PC识别录制的歌曲样本时,Dejavu效果很好,但是对于智能手机,我无法弄清楚如何使用“ micRecognition”。我用智能手机尝试了带有录制歌曲的“ fileRecognition”:我将样本保存在文件中,然后将其发送到dejavu进行工作的服务器上,但是显然,由于质量/背景噪音,它可以识别不同的歌曲。

因此,我尝试使用两个插件: -PhoneGap媒体流插件,但是我不知道在录制音频时如何使用它; -cordova-plugin-audioinput,但在这种情况下,我无法理解此插件是否可以实现我想要的功能。

这是dejavu MicrophoneRecognizer,我正在使用python 2.7在centos 6.9上运行

class MicrophoneRecognizer(BaseRecognizer):
default_chunksize   = 8192
default_format      = pyaudio.paInt16
default_channels    = 2
default_samplerate  = 44100
def __init__(self, dejavu):
    super(MicrophoneRecognizer, self).__init__(dejavu)
    self.audio = pyaudio.PyAudio()
    self.stream = None
    self.data = []
    self.channels = MicrophoneRecognizer.default_channels
    self.chunksize = MicrophoneRecognizer.default_chunksize
    self.samplerate = MicrophoneRecognizer.default_samplerate
    self.recorded = False

def start_recording(self, channels=default_channels,
                    samplerate=default_samplerate,
                    chunksize=default_chunksize):
    self.chunksize = chunksize
    self.channels = channels
    self.recorded = False
    self.samplerate = samplerate

    if self.stream:
        self.stream.stop_stream()
        self.stream.close()

    self.stream = self.audio.open(
        format=self.default_format,
        channels=channels,
        rate=samplerate,
        input=True,
        frames_per_buffer=chunksize,
    )

    self.data = [[] for i in range(channels)]

def process_recording(self):
    data = self.stream.read(self.chunksize)
    nums = np.fromstring(data, np.int16)
    for c in range(self.channels):
        self.data[c].extend(nums[c::self.channels])

def stop_recording(self):
    self.stream.stop_stream()
    self.stream.close()
    self.stream = None
    self.recorded = True

def recognize_recording(self):
    if not self.recorded:
        raise NoRecordingError("Recording was not complete/begun")
    return self._recognize(*self.data)

def get_recorded_time(self):
    return len(self.data[0]) / self.rate

def recognize(self, seconds=10):
    self.start_recording()
    for i in range(0, int(self.samplerate / self.chunksize
                          * seconds)):
        self.process_recording()
    self.stop_recording()
    return self.recognize_recording()

0 个答案:

没有答案