# Play audio p = pyaudio.PyAudio() stream = p.open(format=pyaudio.paInt16, channels=1, rate=sample_rate, output=True)
# Ensure that highest value is in 16-bit range audio = wave / 255.0 * (2**15 - 1) audio = audio.astype(np.int16)
stream.write(audio)
# Parameters sample_rate = 44100 duration = 10 # seconds
# Generate sound t = np.arange(int(sample_rate * duration)) wave = np.array([bytebeat(i) for i in t], dtype=np.uint8) midi to bytebeat
# Simple Bytebeat-like pattern def bytebeat(t): return (t * 3) % 255
import numpy as np import pyaudio
stream.stop_stream() stream.close() p.terminate() This example doesn't convert MIDI files but shows how mathematical expressions can generate sound. Converting MIDI to Bytebeat offers an intriguing exploration into algorithmic music generation. It bridges structured musical data (MIDI) with dynamic, computational sound generation (Bytebeat), allowing for creative and efficient music production techniques. The conversion process encourages a deeper understanding of both the source musical data and the target generative algorithms.
# Play audio p = pyaudio.PyAudio() stream = p.open(format=pyaudio.paInt16, channels=1, rate=sample_rate, output=True)
# Ensure that highest value is in 16-bit range audio = wave / 255.0 * (2**15 - 1) audio = audio.astype(np.int16)
stream.write(audio)
# Parameters sample_rate = 44100 duration = 10 # seconds
# Generate sound t = np.arange(int(sample_rate * duration)) wave = np.array([bytebeat(i) for i in t], dtype=np.uint8)
# Simple Bytebeat-like pattern def bytebeat(t): return (t * 3) % 255
import numpy as np import pyaudio
stream.stop_stream() stream.close() p.terminate() This example doesn't convert MIDI files but shows how mathematical expressions can generate sound. Converting MIDI to Bytebeat offers an intriguing exploration into algorithmic music generation. It bridges structured musical data (MIDI) with dynamic, computational sound generation (Bytebeat), allowing for creative and efficient music production techniques. The conversion process encourages a deeper understanding of both the source musical data and the target generative algorithms.
