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分享音频检测代码：
import sounddevice as sd
import numpy as np
import logging
# 配置日志
logging.basicConfig(level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s",
filename='detect_mute.log',
filemode='a')
# 配置参数
THRESHOLD_DB = -40 # 静音阈值（分贝）
CHUNK_SIZE = 1024 # 每次处理的音频块大小
SAMPLE_RATE = 44100 # 采样率
CHANNELS = 1 # 麦克风通道数
# 计算分贝的函数
def calculate_db(data):
rms = np.sqrt(np.mean(np.square(data.astype(float))))
return 20 * np.log10(rms) if rms > 0 else -np.inf
# 实时音频回调函数
def audio_callback(indata, frames, time, status):
db_level = calculate_db(indata)
is_silent = db_level < THRESHOLD_DB
log_message = f"当前音量: {db_level:.2f} dB | {'静音中...' if is_silent else '检测到声音'}"
print(log_message)
logging.info(log_message)
# 开始监听
try:
logging.info("开始检测静音（按Ctrl+C停止）...")
print("开始检测静音（按Ctrl+C停止）...")
with sd.InputStream(callback=audio_callback,
blocksize=CHUNK_SIZE,
samplerate=SAMPLE_RATE,
channels=CHANNELS):
while True:
sd.sleep(1000) # 保持程序运行
except KeyboardInterrupt:
logging.info("检测已停止")
print("\n检测已停止")
except Exception as e:
logging.error(f"发生错误: {str(e)}")
print(f"发生错误: {str(e)}")
OR
import sounddevice as sd
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from collections import deque
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.widgets import Slider, Button
# 配置参数
SAMPLE_RATE = 44100
CHUNK_SIZE = 1024
BUFFER_SECONDS = 2 # 波形显示时间窗口
NOISE_THRESHOLD_DB = -40 # 初始噪声阈值
FREQ = 440 # 测试音频率
# 生成测试音信号
t = np.arange(CHUNK_SIZE) / SAMPLE_RATE
test_tone = 0.3 * np.sin(2 * np.pi * FREQ * t).astype('float32')
# 可视化参数
plt.rcParams['toolbar'] = 'None' # 隐藏工具栏
WAVE_COLOR = '#1f77b4' # 波形颜色
SPEC_COLOR = 'viridis' # 频谱图颜色
class AudioVisualizer:
def __init__(self):
# 初始化数据缓冲区
self.wave_buffer = deque(maxlen=int(SAMPLE_RATE * BUFFER_SECONDS / CHUNK_SIZE))
self.fft_buffer = deque(maxlen=50) # 用于3D频谱
self.vol_diff_history = deque(maxlen=50)
# 创建可视化画布
self.fig = plt.figure(figsize=(14, 10), facecolor='#f0f0f0')
self.fig.canvas.manager.set_window_title('Real-Time Audio Mixer Analyzer')
# 创建子图布局
self.ax_wave = plt.subplot2grid((3, 3), (0, 0), colspan=3)
self.ax_spec = plt.subplot2grid((3, 3), (1, 0))
self.ax_3d = self.fig.add_subplot(1, 3, 2, projection='3d') # 3D频谱图
self.ax_vol = plt.subplot2grid((3, 3), (1, 2))
self.ax_status = plt.subplot2grid((3, 3), (2, 0), colspan=3)
# 初始化波形图
self.wave_line, = self.ax_wave.plot([], [], lw=1, color=WAVE_COLOR)
self.ax_wave.set_title('Real-time Waveform')
self.ax_wave.set_ylim(-1, 1)
self.ax_wave.set_xlim(0, BUFFER_SECONDS)
# 初始化2D频谱图
self.spec_plot = self.ax_spec.imshow(np.zeros((64, CHUNK_SIZE // 2)),
cmap=SPEC_COLOR,
aspect='auto',
origin='lower',
extent=(0, SAMPLE_RATE / 2, 0, 64))
self.ax_spec.set_title('2D Spectrogram')
self.ax_spec.set_xlabel('Frequency (Hz)')
self.ax_spec.set_ylabel('Time Frame')
# 初始化3D频谱图
self.ax_3d.set_title('3D Spectrogram')
self.ax_3d.set_xlabel('Frequency (Hz)')
self.ax_3d.set_ylabel('Time Frame')
self.ax_3d.set_zlabel('Magnitude')
self.ax_3d.grid(True)
# 初始化音量差仪表
self.vol_bars = self.ax_vol.barh(['Mic', 'Tone'], [0, 0], color=['#2ca02c', '#d62728'])
self.ax_vol.set_title('Volume Comparison')
self.ax_vol.set_xlim(0, 80)
# 状态显示区域
self.status_text = self.ax_status.text(0.5, 0.5, '',
ha='center', va='center', fontsize=14)
self.ax_status.axis('off')
# 添加交互式控制
self.add_interactive_controls()
# 调整布局
plt.tight_layout()
def add_interactive_controls(self):
""" 添加交互式控制组件 """
# 噪声阈值滑块
ax_thresh = plt.axes([0.25, 0.02, 0.5, 0.03], facecolor='lightgoldenrodyellow')
self.threshold_slider = Slider(ax_thresh, 'Noise Threshold (dB)', -60, 0, valinit=NOISE_THRESHOLD_DB)
# 重置按钮
reset_ax = plt.axes([0.8, 0.02, 0.1, 0.03])
self.reset_button = Button(reset_ax, 'Reset', color='lightgoldenrodyellow')
# 绑定事件
self.threshold_slider.on_changed(self.update_threshold)
self.reset_button.on_clicked(self.reset_visualization)
def update_threshold(self, val):
""" 更新噪声阈值 """
global NOISE_THRESHOLD_DB
NOISE_THRESHOLD_DB = val
def reset_visualization(self, event):
""" 重置可视化 """
self.wave_buffer.clear()
self.fft_buffer.clear()
self.vol_diff_history.clear()
self.threshold_slider.reset()
def update_plots(self, frame):
""" 更新所有可视化元素 """
if self.wave_buffer:
# 更新波形图
wave_data = np.concatenate(self.wave_buffer)
time_axis = np.linspace(0, BUFFER_SECONDS, len(wave_data))
self.wave_line.set_data(time_axis, wave_data)
# 更新2D频谱图
spec_data = np.array(self.fft_buffer).T
if spec_data.size > 0:
self.spec_plot.set_data(spec_data)
self.spec_plot.set_clim(vmin=spec_data.min(), vmax=spec_data.max())
# 更新3D频谱图
self.ax_3d.clear()
if len(self.fft_buffer) > 1:
X, Y = np.meshgrid(np.linspace(0, SAMPLE_RATE / 2, CHUNK_SIZE // 2), np.arange(len(self.fft_buffer)))
Z = np.array(self.fft_buffer)
self.ax_3d.plot_surface(X, Y, Z, cmap='magma', rstride=1, cstride=1, alpha=0.8)
self.ax_3d.set_zlim(0, np.max(Z))
# 更新音量仪表
for bar, val in zip(self.vol_bars, [analyzer.mic_db, analyzer.tone_db]):
bar.set_width(val + 60) # 将dB转换为正数显示
self.ax_vol.set_xlim(0, max(analyzer.mic_db, analyzer.tone_db) + 65)
# 更新状态文本
status_str = (f"Noise: {'🚩 Detected' if analyzer.noise_state else '✅ Clean'}\n"
f"Mix: {'🔊 Active' if analyzer.mix_state else '🔇 Inactive'}\n"
f"Vol Diff: {analyzer.volume_diff:.1f} dB")
self.status_text.set_text(status_str)
return [self.wave_line, self.spec_plot, self.status_text] + list(self.vol_bars)
class AudioAnalyzer:
def __init__(self):
self.noise_state = False
self.mix_state = False
self.volume_diff = 0
self.mic_db = 0
self.tone_db = 0
def analyze(self, mic_data, mixed_data):
# 噪声检测
mic_rms = np.sqrt(np.mean(mic_data ** 2))
self.mic_db = 20 * np.log10(mic_rms) if mic_rms > 0 else -np.inf
self.noise_state = self.mic_db > NOISE_THRESHOLD_DB
# 混音检测
freqs, magnitudes = self.calculate_fft(mixed_data)
tone_peak = magnitudes[np.argmin(np.abs(freqs - FREQ))]
avg_magnitude = np.mean(magnitudes)
self.mix_state = tone_peak > 3 * avg_magnitude
# 音量差计算
tone_rms = np.sqrt(np.mean(test_tone ** 2))
self.tone_db = 20 * np.log10(tone_rms) if tone_rms > 0 else -np.inf
self.volume_diff = self.mic_db - self.tone_db
# 保存可视化数据
visualizer.wave_buffer.append(mixed_data)
visualizer.fft_buffer.append(magnitudes[:CHUNK_SIZE // 2])
visualizer.vol_diff_history.append(self.volume_diff)
def calculate_fft(self, data):
fft_data = np.fft.rfft(data)
freqs = np.fft.rfftfreq(len(data), 1 / SAMPLE_RATE)
return freqs, np.abs(fft_data)
def audio_callback(indata, outdata, frames, time, status):
global analyzer
# 混音处理
mixed = (indata[:, 0] * 0.7) + (test_tone * 0.3)
outdata[:] = np.repeat(mixed.reshape(-1, 1), 2, axis=1)
# 音频分析
analyzer.analyze(indata[:, 0], mixed)
if __name__ == "__main__":
# 初始化组件
analyzer = AudioAnalyzer()
visualizer = AudioVisualizer()
# 启动动画
ani = FuncAnimation(visualizer.fig,
visualizer.update_plots,
interval=50,
blit=True)
try:
print("Starting real-time audio analysis with visualization...")
with sd.Stream(samplerate=SAMPLE_RATE,
blocksize=CHUNK_SIZE,
channels=2,
callback=audio_callback):
plt.show() # 阻塞主线程显示窗口
except KeyboardInterrupt:
print("\nAnalysis stopped.") |
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