KI-Integration in Digital Audio Workstations (DAWs)¶

import librosa
import numpy as np
import soundfile as sf

def ki_mixing_assistent(audio_files, output_file):
    """Einfacher KI-Mixing-Assistent für mehrere Spuren."""

    # Alle Spuren laden
    tracks = []
    sample_rates = []

    for file in audio_files:
        y, sr = librosa.load(file, sr=None)
        tracks.append(y)
        sample_rates.append(sr)

    # Gemeinsame Sample-Rate
    target_sr = max(sample_rates)

    # Alle Spuren auf gleiche Sample-Rate bringen
    resampled_tracks = []
    for y, sr in zip(tracks, sample_rates):
        if sr != target_sr:
            y_resampled = librosa.resample(y, orig_sr=sr, target_sr=target_sr)
        else:
            y_resampled = y
        resampled_tracks.append(y_resampled)

    # Normalisierung jedes Tracks
    normalized_tracks = []
    for y in resampled_tracks:
        y_normalized = librosa.util.normalize(y)
        normalized_tracks.append(y_normalized)

    # Einfaches Mixing: Alle Spuren gleich laut
    mixed = np.sum(normalized_tracks, axis=0)

    # Normalisierung des Mix
    mixed = librosa.util.normalize(mixed)

    # Speichern
    sf.write(output_file, mixed, target_sr)
    print(f"Mix gespeichert als {output_file}")

# Verwendung
ki_mixing_assistent(
    ['vocals.wav', 'guitar.wav', 'bass.wav', 'drums.wav'],
    'mix_output.wav'
)

import numpy as np
import mido
from mido import Message, MidiFile, MidiTrack

def generate_drum_pattern(bpm=120, bars=4, output_file='drums.mid'):
    """Generiert ein einfaches Drum-Pattern im MIDI-Format."""

    # MIDI-Datei erstellen
    mid = MidiFile()
    track = MidiTrack()
    mid.tracks.append(track)

    # Tempo setzen (Mikrosekunden pro Beat)
    ticks_per_beat = mid.ticks_per_beat
    microseconds_per_beat = mido.bpm2tempo(bpm)

    # Drum-Mapping (General MIDI)
    kick = 36
    snare = 38
    hi_hat_closed = 42
    hi_hat_open = 46

    # Pattern pro Takt (16tel-Note Resolution)
    pattern = [
        [kick, None, hi_hat_closed, None],      # Beat 1
        [None, snare, hi_hat_closed, None],     # Beat 2
        [kick, None, hi_hat_closed, None],      # Beat 3
        [None, snare, hi_hat_open, None]        # Beat 4
    ]

    # MIDI-Nachrichten hinzufügen
    time = 0

    for bar in range(bars):
        for beat in range(4):
            for sixteenth in range(4):
                drum = pattern[beat][sixteenth]
                if drum is not None:
                    # Note On
                    track.append(Message('note_on', note=drum, velocity=100, time=time))
                    time = 0
                    # Note Off (kurze Dauer für Drums)
                    track.append(Message('note_off', note=drum, velocity=100, time=16))
                else:
                    time += 16

        # Ende des Takts
        time = 0

    # Datei speichern
    mid.save(output_file)
    print(f"Drum-Pattern gespeichert als {output_file}")

# Verwendung
generate_drum_pattern(bpm=120, bars=8, output_file='drum_pattern.mid')

import ddsp
import tensorflow as tf

# Modell laden
model = ddsp.models.Autoencoder()
model.restore('path/to/model checkpoint')

# Audio generieren
audio = model.generate()

import librosa
import numpy as np
import soundfile as sf

def pitch_correction(audio_file, output_file, target_notes):
    """Einfache Pitch-Korrektur für Vocals."""

    # Audio laden
    y, sr = librosa.load(audio_file, sr=None)

    # Pitch extrahieren
    pitches, magnitudes = librosa.piptrack(y=y, sr=sr)

    # Pitch anpassen (vereinfacht)
    # In einer echten Implementierung würde man hier:
    # 1. Die tonale Höhe der Stimme extrahieren
    # 2. Mit den Zielnoten vergleichen
    # 3. Die Stimme auf die Zielnoten verschieben

    # Für dieses Beispiel: Einfache Pitch-Verschiebung
    y_shifted = librosa.effects.pitch_shift(y, sr=sr, n_steps=2)

    # Speichern
    sf.write(output_file, y_shifted, sr)
    print(f"Pitch-korrigierte Audio gespeichert als {output_file}")

# Verwendung
pitch_correction('vocals.wav', 'vocals_corrected.wav', ['C4', 'E4', 'G4'])

import librosa
import numpy as np
import soundfile as sf
from scipy import signal

def ki_mastering(audio_file, output_file):
    """Einfacher KI-Mastering-Prozess."""

    # Audio laden
    y, sr = librosa.load(audio_file, sr=None)

    # 1. Normalisierung
    y = librosa.util.normalize(y)

    # 2. EQ - leichter Bass-Boost
    # FIR-Filter für Hochpass bei 30Hz
    b, a = signal.butter(4, 30, btype='highpass', fs=sr)
    y = signal.filtfilt(b, a, y)

    # 3. Leichte Kompression
    y_compressed = np.copy(y)
    threshold = 0.5
    ratio = 2.0

    for i in range(len(y_compressed)):
        if abs(y_compressed[i]) > threshold:
            y_compressed[i] = np.sign(y_compressed[i]) * (
                threshold + (abs(y_compressed[i]) - threshold) / ratio
            )

    # 4. Limiting
    max_peak = 0.9
    y_limited = np.clip(y_compressed, -max_peak, max_peak)

    # 5. Finaler Boost
    y_final = y_limited * 0.95

    # Speichern
    sf.write(output_file, y_final, sr)
    print(f"Mastered Audio gespeichert als {output_file}")

# Verwendung
ki_mastering('mix.wav', 'mastered.wav')

import librosa
import numpy as np
import pandas as pd

def comprehensive_audio_analysis(audio_file):
    """Umfassende Analyse von Audio-Dateien."""

    # Audio laden
    y, sr = librosa.load(audio_file, sr=None)

    # 1. Grundlegende Metriken
    duration = librosa.get_duration(y=y, sr=sr)
    rms = np.sqrt(np.mean(librosa.feature.rms(y=y)[0]**2))
    peak = np.max(np.abs(y))
    dynamic_range = 20 * np.log10(peak / rms) if rms > 0 else 0

    # 2. Spektrale Analyse
    spectral_centroid = np.mean(librosa.feature.spectral_centroid(y=y, sr=sr)[0])
    spectral_bandwidth = np.mean(librosa.feature.spectral_bandwidth(y=y, sr=sr)[0])
    spectral_rolloff = np.mean(librosa.feature.spectral_rolloff(y=y, sr=sr)[0])

    # 3. Tonale Analyse
    pitches, magnitudes = librosa.piptrack(y=y, sr=sr)
    pitch_mean = np.mean(pitches[pitches > 0]) if np.any(pitches > 0) else 0

    # 4. Rhythmus-Analyse
    tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)

    # 5. Klangfarbe
    mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
    mfcc_mean = np.mean(mfccs, axis=1)

    # 6. Stereo-Analyse (falls Stereo)
    if len(y.shape) > 1 and y.shape[0] == 2:
        y_left = y[0]
        y_right = y[1]
        stereo_width = np.mean(np.abs(y_left - y_right)) / (np.mean(np.abs(y_left + y_right)) + 1e-10)
    else:
        stereo_width = 0

    return {
        'Datei': audio_file,
        'Dauer (s)': duration,
        'RMS (dB)': 20 * np.log10(rms) if rms > 0 else -np.inf,
        'Peak': peak,
        'Dynamikbereich (dB)': dynamic_range,
        'Spektraler Schwerpunkt (Hz)': spectral_centroid,
        'Spektrale Bandbreite (Hz)': spectral_bandwidth,
        'Spektrale Roll-off (Hz)': spectral_rolloff,
        'Durchschnittliche Tonhöhe (Hz)': pitch_mean,
        'Tempo (BPM)': tempo,
        'Stereo-Breite': stereo_width,
        **{f'MFCC {i+1}': mfcc_mean[i] for i in range(13)}
    }

# Analyse durchführen
result = comprehensive_audio_analysis('mix.wav')
for key, value in result.items():
    print(f"{key}: {value}")