94 lines
3.2 KiB
Python
94 lines
3.2 KiB
Python
"""Settings for audio reactive LED strip"""
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from __future__ import print_function
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from __future__ import division
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import os
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N_PIXELS = 240
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"""Number of pixels in the LED strip (must match ESP8266 firmware)"""
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GAMMA_TABLE_PATH = os.path.join(os.path.dirname(__file__), 'gamma_table.npy')
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"""Location of the gamma correction table"""
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UDP_IP = '192.168.1.150'
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"""IP address of the ESP8266"""
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UDP_PORT = 7777
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"""Port number used for socket communication between Python and ESP8266"""
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MIC_RATE = 44100
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"""Sampling frequency of the microphone in Hz"""
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FPS = 45
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"""Desired LED strip update rate in frames (updates) per second
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This is the desired update rate of the LED strip. The actual refresh rate of
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the LED strip may be lower if the time needed for signal processing exceeds
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the per-frame recording time.
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A high FPS results in low latency and smooth animations, but it also reduces
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the duration of the short-time Fourier transform. This can negatively affect
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low frequency (bass) response.
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"""
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ENERGY_THRESHOLD = 14.0
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"""Energy threshold for determining whether a beat has been detected
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One aspect of beat detection is comparing the current energy of a frequency
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subband to the average energy of the subband over some time interval. Beats
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are often associated with large spikes in energy relative to the recent
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average energy.
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ENERGY_THRESHOLD is the threshold used to determine if the energy spike is
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sufficiently large to be considered a beat.
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For example, if ENERGY_THRESHOLD = 2, then a beat is detected if the current
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frequency subband energy is more than 2 times the recent average energy.
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"""
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VARIANCE_THRESHOLD = 0.0
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"""Variance threshold for determining whether a beat has been detected
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Beat detection is largely determined by the ENERGY_THRESHOLD, but we can also
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require frequency bands to have a certain minimum variance over some past
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time interval before a beat can be detected.
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One downside to using a variance threshold is that it is an absolute threshold
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which is affected by the current volume.
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"""
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N_SUBBANDS = 80 # 240 #48
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"""Number of frequency bins to use for beat detection
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More subbands improve beat detection sensitivity but it may become more
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challenging for the visualization to work for a wide range of music.
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Fewer subbands reduces signal processing time at the expense of beat detection
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sensitivity.
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"""
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N_HISTORY = int(0.8 * FPS)
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"""Number of previous samples to consider when doing beat detection
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Beats are detected by comparing the most recent audio recording to a collection
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of previous audio recordings. This is the number of previous audio recordings
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to consider when doing beat detection.
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For example, setting N_HISTORY = int(1.0 * config.FPS) means that one second
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of previous audio recordings will be used for beat detection.
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Smaller values reduces signal processing time but values too small may reduce
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beat detection accuracy. Larger values increase signal processing time and
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values too large can also reduce beat detection accuracy. Roughly one second
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of previous data tends to work well.
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"""
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GAMMA_CORRECTION = True
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"""Whether to correct LED brightness for nonlinear brightness perception"""
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N_CURVES = 4
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"""Number of curves to plot in the visualization window"""
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N_ROLLING_HISTORY = 2
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"""Number of past audio frames to include in the rolling window""" |