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Machine-Sound-Anomaly-Detector / features /mel_spectrogram.py

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"""
===============================================================================
features/mel_spectrogram.py — Mel Spectrogram Extraction
===============================================================================
"""

import numpy as np
import librosa
from config import N_MELS, N_FFT, HOP_LENGTH, FMAX, MEL_POWER

N_MELS = 128
N_FFT = 1024
HOP_LENGTH = 512
FMAX = 8000
MEL_POWER = 2.0  # 2.0 = power spectrogram (standard), 1.0 = energy spectrogram

# Toggle whether to scale the final output dB array to exactly [0.0, 1.0]
# HIGHLY RECOMMENDED for CNN inputs to keep gradients stable.
# why did I choose to use min-max scaling here? 
# because the log-mel spectrogram can have a wide range of values depending on the loudness of the audio, 
# and scaling it to [0.0, 1.0] ensures that the CNN receives inputs in a consistent range
# which will make the weights explode in different directions when The data is fed to the CNN
# all weights will be standradized 
APPLY_MIN_MAX_SCALING = True

def extract_mel_spectrogram(audio, sr):
    """
    Extract a log-mel spectrogram from an audio waveform.
    Notes
    -----
    - The output is in log (dB) scale, normalized per-sample.
    - ref=np.max in power_to_db means 0 dB = the loudest point in this sample.
      All other values are negative dB below the peak.
    - This per-sample normalization is important because sala7's augmentation
      thresholds and EL sir's CNN batch normalization depend on consistent
      value ranges.
    """
    mel_spec = librosa.feature.melspectrogram(
          y=audio,
          sr=sr,
          n_fft=N_FFT,
          hop_length=HOP_LENGTH,
          n_mels=N_MELS,
          fmax=FMAX,
          power=MEL_POWER
      )

      # Convert to log scale (dB)
      # Using ref=np.max sets the absolute loudest point in this file to 0 dB, 
      # making all other values negative relative to the peak.
    log_mel_spec = librosa.power_to_db(mel_spec, ref=np.max) # so here I get the loudest point in the array and make it my refrence to be the zero decibel

      # Per-sample normalization to [0.0, 1.0]
    if APPLY_MIN_MAX_SCALING:
        # Prevent division by zero if the file is completely silent
        ptp = log_mel_spec.max() - log_mel_spec.min()
        if ptp > 1e-6:
            log_mel_spec = (log_mel_spec - log_mel_spec.min()) / ptp
        else:
            # If the file is just silent static, zero it out entirely
            log_mel_spec = np.zeros_like(log_mel_spec)

    return log_mel_spec

	"""
	===============================================================================
	features/mel_spectrogram.py — Mel Spectrogram Extraction
	===============================================================================

	"""

	import numpy as np
	import librosa
	from config import N_MELS, N_FFT, HOP_LENGTH, FMAX, MEL_POWER

	N_MELS = 128
	N_FFT = 1024
	HOP_LENGTH = 512
	FMAX = 8000
	MEL_POWER = 2.0 # 2.0 = power spectrogram (standard), 1.0 = energy spectrogram

	# Toggle whether to scale the final output dB array to exactly [0.0, 1.0]
	# HIGHLY RECOMMENDED for CNN inputs to keep gradients stable.
	# why did I choose to use min-max scaling here?
	# because the log-mel spectrogram can have a wide range of values depending on the loudness of the audio,
	# and scaling it to [0.0, 1.0] ensures that the CNN receives inputs in a consistent range
	# which will make the weights explode in different directions when The data is fed to the CNN
	# all weights will be standradized
	APPLY_MIN_MAX_SCALING = True

	def extract_mel_spectrogram(audio, sr):
	"""
	Extract a log-mel spectrogram from an audio waveform.

	Notes
	-----
	- The output is in log (dB) scale, normalized per-sample.
	- ref=np.max in power_to_db means 0 dB = the loudest point in this sample.
	All other values are negative dB below the peak.
	- This per-sample normalization is important because sala7's augmentation
	thresholds and EL sir's CNN batch normalization depend on consistent
	value ranges.

	"""
	mel_spec = librosa.feature.melspectrogram(
	y=audio,
	sr=sr,
	n_fft=N_FFT,
	hop_length=HOP_LENGTH,
	n_mels=N_MELS,
	fmax=FMAX,
	power=MEL_POWER
	)

	# Convert to log scale (dB)
	# Using ref=np.max sets the absolute loudest point in this file to 0 dB,
	# making all other values negative relative to the peak.
	log_mel_spec = librosa.power_to_db(mel_spec, ref=np.max) # so here I get the loudest point in the array and make it my refrence to be the zero decibel

	# Per-sample normalization to [0.0, 1.0]
	if APPLY_MIN_MAX_SCALING:
	# Prevent division by zero if the file is completely silent
	ptp = log_mel_spec.max() - log_mel_spec.min()
	if ptp > 1e-6:
	log_mel_spec = (log_mel_spec - log_mel_spec.min()) / ptp
	else:
	# If the file is just silent static, zero it out entirely
	log_mel_spec = np.zeros_like(log_mel_spec)

	return log_mel_spec