使用librosa.filters.mel提取音频中4000hz以上的数据

可以使用以下代码来提取音频中4000hz以上的数据：

import librosa

# load audio file
audio, sr = librosa.load('audio_file.wav', sr=None)

# define mel filterbank parameters
n_fft = 2048
hop_length = 512
n_mels = 128
fmin = 0
fmax = 8000

# calculate mel filterbank
mel_basis = librosa.filters.mel(sr, n_fft, n_mels, fmin, fmax)

# apply mel filterbank to audio
mel_spec = librosa.feature.melspectrogram(audio, sr=sr, n_fft=n_fft, hop_length=hop_length, n_mels=n_mels, fmin=fmin, fmax=fmax)

# convert mel spectrogram to decibels
mel_spec_db = librosa.power_to_db(mel_spec)

# slice mel spectrogram to keep only frequencies above 4000hz
mel_spec_db_high_freq = mel_spec_db[mel_basis[:, 4000/sr*n_fft:].argmax(axis=1)+1:]

# convert mel spectrogram back to linear scale
mel_spec_high_freq = librosa.db_to_power(mel_spec_db_high_freq)

# invert mel filterbank to get back to audio domain
audio_high_freq = librosa.feature.inverse.mel_to_audio(mel_spec_high_freq, sr=sr, n_fft=n_fft, hop_length=hop_length, win_length=n_fft)

# write audio to file
librosa.output.write_wav('audio_file_high_freq.wav', audio_high_freq, sr=sr)

这段代码中，`librosa.filters.mel`函数用于计算mel滤波器组，然后使用`librosa.feature.melspectrogram`函数将音频转换为mel频谱图。接着，我们将mel频谱图转换为分贝单位，并使用切片操作保留4000hz以上的频率范围。最后，我们将分贝单位的mel频谱图转换回线性单位，并使用`librosa.feature.inverse.mel_to_audio`函数将其转换回音频信号。