billy/musicpom
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

patrick

Browse Source
This commit is contained in:
billypom on debian 2025-04-03 21:35:43 -04:00
parent 87137bcd36
commit 87c53c3747
2 changed files with 16 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

7
components/AudioVisualizer.py
View File

@ -1,5 +1,7 @@
import numpy as np
import math
from PyQt5 import QtWidgets
from numpy.lib import math
from utils import FFTAnalyser
@ -67,12 +69,12 @@ class AudioVisualizer(QtWidgets.QWidget):
With a noise floor cutoff at around -96dB (for very small values)
"""
# Avoid log(0) by adding a small epsilon
epsilon = 1e-6
epsilon = 1e-30
amplitudes = np.maximum(self.amps, epsilon)
# Convert to decibels (20*log10 is the standard formula for amplitude to dB)
db_values = 20 * np.log10(amplitudes)
# Clip very low values to have a reasonable floor (e.g. -96dB)
db_values = np.maximum(db_values, -96)
db_values = np.maximum(db_values, -2000)
return db_values
def set_rs(self, rs):
@ -84,4 +86,5 @@ class AudioVisualizer(QtWidgets.QWidget):
Amps are assigned here, based on values passed by the signal
"""
# self.amps = np.maximum(np.array(amps), 1e-12) # Set a very small threshold
# print(self.amps)
self.amps = np.array(amps)

20
utils/fft_analyser.py
View File

@ -27,7 +27,7 @@ class FFTAnalyser(QtCore.QThread):
# in this case, it takes 5% of the samples at some point in time
self.sampling_window_length = 0.05
self.visual_delta_threshold = 1000
self.sensitivity = 1
self.sensitivity = 10
def reset_media(self):
"""Resets the media to the currently playing song."""
@ -65,6 +65,7 @@ class FFTAnalyser(QtCore.QThread):
freq = np.fft.fftfreq(fourier.size, d=self.sampling_window_length)
amps = 2 / v_sample.size * np.abs(fourier)
data = np.array([freq, amps]).T
# TEST:
# print(freq * .05 * self.song.frame_rate)
@ -89,18 +90,18 @@ class FFTAnalyser(QtCore.QThread):
if not data.size:
return
# for i, freq in enumerate(np.arange(0, 1, point_range), start=1):
for i, log_freq in enumerate(log_freqs):
for i, freq in enumerate(np.arange(0, 1, point_range), start=1):
# for i, log_freq in enumerate(log_freqs):
# get the amps which are in between the frequency range
# amps = data[(freq - point_range < data[:, 0]) & (data[:, 0] < freq)]
amps = data[(log_freq - point_range < data[:, 0]) & (data[:, 0] < log_freq)]
amps = data[(freq - point_range < data[:, 0]) & (data[:, 0] < freq)]
# amps = data[(log_freq - point_range < data[:, 0]) & (data[:, 0] < log_freq)]
if not amps.size:
point_samples.append(0)
else:
point_samples.append(
amps.max()
* (
(1 + self.sensitivity / 10 + (self.sensitivity - 1) / 10)
((1 + self.sensitivity) / 10 + (self.sensitivity - 1) / 10)
** (i / 50)
)
)
@ -109,7 +110,7 @@ class FFTAnalyser(QtCore.QThread):
# array (self.points) is so that we can fade out the previous amplitudes from
# the past
for n, amp in enumerate(point_samples):
amp *= 2
# amp *= 2
if self.player.state() in (
self.player.PausedState,
self.player.StoppedState,
@ -124,10 +125,11 @@ class FFTAnalyser(QtCore.QThread):
self.points[n] = amp
# print(f'amp > points[n] - {amp} > {self.points[n]}')
# Set a lower threshold to properly reach zero
if self.points[n] < 1:
self.points[n] = 1e-5
if self.points[n] < 1e-4:
self.points[n] = 0
# print(self.points)
# interpolate points
rs = gaussian_filter1d(self.points, sigma=2)