以同步方式检测节拍并播放 (wav) 文件

Question

我正在尝试使用此节拍检测算法在 python 中进行音频处理。我已经实现了上述文章中的第一个（非优化版本）。虽然它打印了一些结果，但我无法检测它是否具有一定的准确性，因为我不知道如何用它播放声音。

目前，我习惯Popen在进入计算循环之前用歌曲异步启动媒体播放器，但我不确定此策略是否有效并给出同步结果。

#!/usr/bin/python

import scipy.io.wavfile, numpy, sys, subprocess

# Some abstractions for computation
def sumsquared(arr):
    sum = 0
    for i in arr:
            sum = sum + (i[0] * i[0]) + (i[1] * i[1])

    return sum

if sys.argv.__len__() < 2:
    print 'USAGE: wavdsp <wavfile>'
    sys.exit(1)

numpy.set_printoptions(threshold='nan')
rate, data = scipy.io.wavfile.read(sys.argv[1])


# Beat detection algorithm begin 
# the algorithm has been implemented as per GameDev Article
# Initialisation
data_len = data.__len__()
idx = 0
hist_last = 44032
instant_energy = 0
local_energy = 0
le_multi = 0.023219955 # Local energy multiplier ~ 1024/44100


# Play the song
p = subprocess.Popen(['audacious', sys.argv[1]])

while idx < data_len - 48000:
    dat = data[idx:idx+1024]
    history = data[idx:hist_last]
    instant_energy = sumsquared(dat)
    local_energy = le_multi * sumsquared(history)
    print instant_energy, local_energy
    if instant_energy > (local_energy * 1.3):
            print 'Beat'

    idx = idx + 1024
    hist_last = hist_last + 1024 # Right shift history buffer

 p.terminate()

我可以对脚本进行哪些修改/添加，以便以时间同步的方式获取音频输出和算法（控制台）输出？即当控制台输出特定帧的结果时，该帧必须在扬声器上播放。

Answer 1

工作节拍检测代码（NumPy / PyAudio）

如果您使用 NumPy，此代码可能会有所帮助。它假设信号（用 PyAudio 读取）是 16 位宽的 Int。如果情况并非如此，请更改或删除 signal.astype() 并调整归一化分频器（此处最大 int16）。

class SimpleBeatDetection:
    """
    Simple beat detection algorithm from
    http://archive.gamedev.net/archive/reference/programming/features/beatdetection/index.html
    """
    def __init__(self, history = 43):
        self.local_energy = numpy.zeros(history) # a simple ring buffer
        self.local_energy_index = 0 # the index of the oldest element

    def detect_beat(self, signal):

        samples = signal.astype(numpy.int) # make room for squares
        # optimized sum of squares, i.e faster version of (samples**2).sum()
        instant_energy = numpy.dot(samples, samples) / float(0xffffffff) # normalize

        local_energy_average = self.local_energy.mean()
        local_energy_variance = self.local_energy.var()

        beat_sensibility = (-0.0025714 * local_energy_variance) + 1.15142857
        beat = instant_energy > beat_sensibility * local_energy_average

        self.local_energy[self.local_energy_index] = instant_energy
        self.local_energy_index -= 1
        if self.local_energy_index < 0:
            self.local_energy_index = len(self.local_energy) - 1

        return beat

用于 wav 读取或麦克风记录的 PyAudio 示例将为您提供所需的信号数据。使用以下命令高效创建 NumPy 数组frombuffer()

data = stream.read(CHUNK)
signal = numpy.frombuffer(data, numpy.int16)