262206d359
Changes to config.py: > Increased STFT rolling window size > Added configurable options for setting the max and min FFT frequency Changes to dsp.py: > Significantly improved the normalized weighted phase deviation code. This noticeably improves the onset detection accuracy. > Logarithmic partitioning of the FFT bins now happens after onset detection instead of before onset detection. This improves onset detection accuracy at the expense of CPU time. > Fixed a bug in the log_partition function which would sometimes cause out of bounds errors > Refactored and removed some functions that aren't needed anymore Changes to sandbox.py: > Sweeping changes to the visualization functions. The onset detection functions are now combined after applying Gaussian blur to each onset function individually. This improves the overall agreement between the onset functions. > Hyperbolic tan function is used to map saturated pixels to the range [0, 1] > Added a new rainbow generation function with more options than the old one. > Refactored most of the led update functions. > The LED brightness is now being plotted instead of onsets
103 lines
3.4 KiB
Python
103 lines
3.4 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 = 70
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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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MIN_FREQUENCY = 5
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"""Frequencies below this value will be removed during onset detection"""
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MAX_FREQUENCY = 12000
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"""Frequencies above this value will be removed during onset detection"""
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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 = 60 # 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 = 2
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"""Number of curves to plot in the visualization window"""
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N_ROLLING_HISTORY = 8
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"""Number of past audio frames to include in the rolling window""" |