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一册在手——人工智能與音樂下冊（實戰篇）實戰篇一、几個基本模型解釋 1. Magenta：Google開發的機器學習庫，可創建音樂和藝術作品。代碼： ``` from magenta.models.melody_rnn import melody_rnn_sequence_generator # 初始化模型 model = melody_rnn_sequence_generator.MelodyRnnSequenceGenerator( model_checkpoint='path/to/checkpoint', bundle_file='path/to/bundle' ) # 生成音樂序列 generated_sequence = model.generate_sequence() # 保存為MIDI文件 melody_rnn_sequence_generator.sequence_proto_to_midi_file( generated_sequence, 'path/to/generated_sequence.mid' ) ``` 2. MuseNet：OpenAI推出的多领域AI音乐创作程序。代碼： ``` from musenet import MuseNet # 初始化模型 model = MuseNet(isotope_model=True) # 生成音樂序列 generated_sequence = model.generate(n_samples=1, length=1024, temperature=1.0) # 保存為MIDI文件 generated_sequence.to_midi_file('path/to/generated_sequence.mid') ``` 3. NSynth：Google開發的基於神經網絡的合成器，可生成各種優美的聲音。代碼： ``` import tensorflow as tf import librosa import numpy as np # 加載預訓練模型 model = tf.keras.models.load_model('path/to/pretrained_model.h5') # 讀取音頻文件 audio, sr = librosa.load('path/to/audio_file') # 轉換為頻譜矩陣 spectrogram = librosa.core.stft(audio) # 對頻譜矩陣進行高斯平滑處理 spectrogram = librosa.core.times_like(spectrogram) * spectrogram spectrogram = np.log(np.abs(spectrogram) + 1e-7) # 生成音頻文件 generated_audio = model.predict(spectrogram) # 保存為音頻文件 librosa.output.write_wav('path/to/generated_audio.wav', generated_audio, sr) ``` 4. Amper Music：基於人工智能技術的音樂生成平台。代碼： ``` from amper.music.generator import AmperGenerator # 初始化模型 generator = AmperGenerator() # 生成音樂序列 generated_sequence = generator.generate(style='jazz', key='C', tempo=120, duration=120) # 保存為MIDI文件 generated_sequence.write('path/to/generated_sequence.mid') ``` 5. Jukedeck：AI音樂生成器，可創建多種風格的音樂。代碼： ``` from jukedeck import Jukedeck # 初始化模型 jukedeck = Jukedeck() # 生成音樂序列 generated_sequence = jukedeck.generate(tempo=120, duration=60, genre='rock') # 保存為MIDI文件 generated_sequence.write('path/to/generated_sequence.mid') ``` 6. AIVA：人工智能算法驅動的音樂生成器，可生成優質的交響樂。代碼： ``` from aiva import Aiva # 初始化模型 aiva = Aiva() # 生成音樂序列 generated_sequence = aiva.generate(tempo=120, duration=120) # 保存為MIDI文件 generated_sequence.write('path/to/generated_sequence.mid') ``` 7. Amadeus Code：基於人工智能技術的音樂創作工具，可為您創建曲調和歌詞。代碼： ``` from amadeus import Amadeus # 初始化模型 amadeus = Amadeus() # 生成音樂序列 generated_sequence = amadeus.generate(tempo=120, duration=60, key='C', mode='major', style='pop') # 保存為MIDI文件 generated_sequence.write('path/to/generated_sequence.mid') ``` 8. DeepJ：一種基於深度學習技術的音樂生成器，可生成各種樂器和風格的音樂。代碼： ``` from deepj import DeepJ # 初始化模型 deepj = DeepJ() # 生成音樂序列 generated_sequence = deepj.generate(style='jazz', instrument='piano', duration=60) # 保存為MIDI文件 generated_sequence.write('path/to/generated_sequence.mid') ``` 9. Groove Pizza：可在線生成節奏的網站，使用了人工智能技術。代碼： ``` import requests # 構建URL url = 'https://api.groovepizza.com/get_analog.pattern_json?beats=4&bpm=120&cells=16' # 發送請求 response = requests.get(url) # 解析JSON數據 data = response.json() # 保存為MIDI文件 data['midi'].write('path/to/generated_sequence.mid') ``` 10. AIComposer：一款基於人工智能的音樂生成器，可生成古典和流行的音樂。代碼： ``` from aicomposer import AIComposer # 初始化模型 composer = AIComposer() # 生成音樂序列 generated_sequence = composer.generate(style='jazz', key='C', tempo=120, duration=60) # 保存為MIDI文件 generated_sequence.write('path/to/generated_sequence.mid') ``` 這些代碼僅供參考，實際使用需要根據自己的需求進行修改。二、幾个基本和简短的人工智能音乐程序实例 1. Python音乐生成器这个音乐生成器利用Python语言和音乐理论的知识来生成各种乐器的音乐。通过几个简单的函数，它可以生成和弦，和声，节奏和旋律等基本元素。让我们看看下面的代码: ```Python # 定义和弦 def generate_chord(root_note, chord_type): chord_notes = [] if chord_type == "major": chord_notes = [root_note, root_note + 4, root_note + 7] elif chord_type == "minor": chord_notes = [root_note, root_note + 3, root_note + 7] elif chord_type == "diminished": chord_notes = [root_note, root_note + 3, root_note + 6] return chord_notes # 定义旋律 def generate_melody(chords, duration): melody_notes = [] for chord in chords: melody_notes += [chord[0], chord[1], chord[2], chord[1]] melody_notes += [melody_notes[-1]] * 2 return melody_notes # 定义和声 def generate_harmony(chords, duration): harmony_notes = [] for chord in chords: chord_notes = generate_chord(chord[0], chord[1]) for note in chord_notes: harmony_notes += [note] * duration return harmony_notes # 主程序 def generate_music(chord_progression, duration): chords = [] for chord in chord_progression: chords.append((chord[0], chord[1])) melody = generate_melody(chords, duration) harmony = generate_harmony(chords, duration) return melody, harmony # 使用 chord_progression = [(60, "major"), (62, "minor"), (64, "minor"), (65, "major")] duration = 4 melody, harmony = generate_music(chord_progression, duration) ``` 这是一个简单的程序，可以生成一个和弦进行的旋律和和声。你也可以修改旋律和和声的算法来生成其他类型的音乐元素。 2. 基于神经网络的钢琴即兴演奏这个程序使用基于神经网络的模型来进行钢琴即兴演奏。它首先训练一个神经网络模型来学习以前的钢琴即兴演奏，然后使用模型来生成新的即兴演奏。以下是该程序的代码示例: ```Python # 导入必要的库 import numpy as np import os import keras.backend as K from keras.callbacks import ModelCheckpoint from keras.layers import Input, LSTM, Dense, Dropout, Activation from keras.models import Model from keras.optimizers import Adam from keras.utils import np_utils # 定义模型参数 input_shape = (100, 5) num_lstm_units = 512 num_dense_units = 256 dropout_rate = 0.2 learning_rate = 0.001 batch_size = 64 num_epochs = 200 # 加载钢琴即兴演奏数据 def load_data(data_dir): notes = [] for file in os.listdir(data_dir): if file.endswith('.npz'): path = os.path.join(data_dir, file) with np.load(path) as data: notes.append(data['arr_0']) return np.concatenate(notes) # 建立RNN模型 def build_model(input_shape): inputs = Input(input_shape) x = LSTM(num_lstm_units, return_sequences=True)(inputs) x = Dropout(dropout_rate)(x) x = LSTM(num_lstm_units)(x) x = Dropout(dropout_rate)(x) x = Dense(num_dense_units)(x) x = Dropout(dropout_rate)(x) x = Activation('relu')(x) output = Dense(1, activation='sigmoid')(x) model = Model(inputs=inputs, outputs=output) optimizer = Adam(lr=learning_rate) model.compile(loss='binary_crossentropy', optimizer=optimizer) return model # 训练模型 def train_model(model, X_train, y_train, batch_size, num_epochs): checkpoint = ModelCheckpoint( filepath='./weights.best.hdf5', verbose=1, save_best_only=True) model.fit(X_train, y_train, batch_size=batch_size, epochs=num_epochs, callbacks=[checkpoint]) # 生成新的即兴演奏 def generate_improvisation(model, input_notes, length): predicted_notes = [] for i in range(length): x = np.reshape(input_notes, (1, input_notes.shape[0], input_notes.shape[1])) prediction = model.predict(x, verbose=0) predicted_notes.append(prediction[0][0]) input_notes = np.concatenate( [input_notes[1:, :], np.array([[prediction[0][0]]])], axis=0) return predicted_notes # 使用 data_dir = './data/' notes = load_data(data_dir) model = build_model(input_shape) train_model(model, X_train, y_train, batch_size, num_epochs) ``` 这个程序使用基于Keras库的神经网络模型来生成新的钢琴即兴演奏。通过加载已有的钢琴即兴演奏数据集并训练一个模型，程序可以生成新的即兴演奏。你可以尝试修改模型的参数和数据集来生成不同类型的钢琴即兴演奏。 3. 基于AI的鼓机这个程序使用AI来生成不同种类的鼓乐器的鼓声。它的核心是一个递归神经网络，可以生成不同类型的鼓声例子。以下是该程序的代码示例: ```Python # 导入必要的库 import numpy as np from keras.layers import LSTM, Dropout, Dense, Activation from keras.models import Sequential from keras.utils import np_utils # 定义参数 num_loopbeats = 4 num_notes_per_loopbeat = 16 num_output_notes = num_loopbeats * num_notes_per_loopbeat * 4 # 加载数据集 def load_data(data_dir): with open(data_dir, 'r') as f: data = f.readlines() return data # 编码数据 def encode_data(data, char_to_int): encoded = [char_to_int[c] for c in data if c != '\n'] X = [] y = [] for i in range(0, len(encoded) - num_output_notes, num_output_notes): X.append(encoded[i:i + num_output_notes]) y.append(encoded[i + num_output_notes]) X = np.reshape(X, (len(X), num_output_notes, 1)) X = X / float(len(char_to_int)) y = np_utils.to_categorical(y) return X, y # 构建模型 def build_model(X, y, num_classes): model = Sequential() model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2]), return_sequences=True)) model.add(Dropout(0.2)) model.add(LSTM(256)) model.add(Dropout(0.2)) model.add(Dense(num_classes)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam') return model # 生成音乐 def generate_music(model, data, int_to_char, num_chars, seed_notes, num_notes): output_notes = [] for i in range(num_notes): x = np.reshape(seed_notes, (1, len(seed_notes), 1)) x = x / float(num_chars) prediction = model.predict(x, verbose=0) index = np.argmax(prediction) result = int_to_char[index] output_notes.append(result) seed_notes.append(index) seed_notes = seed_notes[1:len(seed_notes)] return ''.join(output_notes) # 使用 data_dir = 'drumbeats.txt' data = load_data(data_dir) chars = sorted(list(set(data))) char_to_int = dict((c, i) for i, c in enumerate(chars)) int_to_char = dict((i, c) for i, c in enumerate(chars)) num_chars = len(chars) X, y = encode_data(data, char_to_int) model = build_model(X, y, num_chars) model.fit(X, y, epochs=100, batch_size=128, verbose=2) start = np.random.randint(0, len(X)-1) seed_notes = list(X[start, :, 0]) output = generate_music(model, data, int_to_char, num_chars, seed_notes, num_output_notes) ``` 这个程序使用递归神经网络来生成不同类型的鼓声例子，通过训练数据中的鼓声和一个递归神经网络模型来完成。你可以尝试添加不同类型的鼓声数据集和修改模型的参数来生成不同类型的鼓声例子。 4. 基于GAN的电子舞曲生成器这个程序使用生成对抗网络（GAN）来生成不同类型的电子舞曲。GAN有两个部分：生成器和判别器。生成器负责生成新的电子舞曲曲目，而判别器则负责评估生成的电子舞曲曲目是否与真正的电子舞曲曲目相似，然后再改进生成器。以下是该程序的代码示例: ```Python # 导入必要的库 import os import numpy as np from keras.datasets import mnist from keras.layers import Input from keras.layers.advanced_activations import LeakyReLU from keras.layers.convolutional import UpSampling2D, Conv2D from keras.models import Model, Sequential from keras.optimizers import Adam # 定义参数 img_rows = 128 img_cols = 128 channels = 3 # 创建对抗网络 def build_gan(generator, discriminator): # 固定判别器倒数第二层之前的参数 discriminator.trainable = False # 将生成器的输出作为判别器的输入 gan_input = Input(shape=(100,)) x = generator(gan_input) gan_output = discriminator(x) gan = Model(inputs=gan_input, outputs=gan_output) optimizer = Adam(0.0002, 0.5) gan.compile(loss='binary_crossentropy', optimizer=optimizer) return gan # 构建判别器模型 def build_discriminator(): model = Sequential() model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=(img_rows, img_cols, channels), padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(64, kernel_size=3, strides=2, padding="same")) model.add(ZeroPadding2D(padding=((0,1),(0,1)))) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(128, kernel_size=3, strides=2, padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(256, kernel_size=3, strides=2, padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(1, activation='sigmoid')) model.summary() img = Input(shape=(img_rows, img_cols, channels)) validity = model(img) model = Model(inputs=img, outputs=validity) optimizer = Adam(0.0002, 0.5) model.compile(loss='binary_crossentropy', optimizer=optimizer) return model # 构建生成器模型 def build_generator(): model = Sequential() model.add(Dense(128 * 16 * 16, activation="relu", input_dim=100)) model.add(Reshape((16, 16, 128))) model.add(UpSampling2D()) model.add(Conv2D(128, kernel_size=3, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(Activation("relu")) model.add(UpSampling2D()) model.add(Conv2D(64, kernel_size=3, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(Activation("relu")) model.add(Conv2D(channels, kernel_size=3, padding="same")) model.add(Activation("tanh")) model.summary() noise = Input(shape=(100,)) img = model(noise) model = Model(inputs=noise, outputs=img) return model # 加载数据集 def load_data(): X_train = [] for filename in os.listdir("data/"): image = imread("data/" + filename) if image.shape != (img_rows, img_cols, channels): image = imresize(image, (img_rows, img_cols, channels)) X_train.append(image) X_train = np.asarray(X_train) X_train = X_train / 127.5 - 1. return X_train # 使用 X_train = load_data() generator = build_generator() discriminator = build_discriminator() gan = build_gan(generator, discriminator) ``` 这个程序使用基于Keras库的GAN来生成不同类型的电子舞曲。程序分别构造了一个生成器和一个判别器来完成任务。你可以修改模型的参数和数据集来生成不同类型的电子舞曲。 5. 基于LSTM的复调音乐生成器这个程序使用基于LSTM的模型来生成复调音乐。它的核心是一个两层的LSTM模型，它学习并生成新的复调音乐片段。以下是该程序的代码示例: ```Python # 导入必要的库 import numpy as np import pandas as pd from keras.callbacks import ModelCheckpoint from keras.layers import LSTM, Input, Dense from keras.models import Model # 定义参数 sequence_length = 64 num_units = 256 num_epochs = 100 # 加载数据集 def load_data(data_dir): notes = pd.read_csv(data_dir) notes = notes[notes['type'].isin(['note', 'rest'])] return np.asarray(notes[['duration', 'quarterLength', 'pitch']].values) # 编码数据 def encode_data(notes, pitch_to_int): encoded_notes = [] for note in notes: # 编码音高 pitch = note[2] pitch_class = pitch % 12 encoded_pitch = pitch_to_int[pitch_class] duration = note[0] quarter_length = note[1] encoded_notes.append([encoded_pitch, duration, quarter_length]) return encoded_notes # 构建LSTM模型 def build_model(sequence_length, num_units): inputs = Input(shape=(sequence_length, 3)) x = LSTM(num_units, return_sequences=True)(inputs) x = LSTM(num_units)(x) x = Dense(3)(x) model = Model(inputs=inputs, outputs=x) model.compile(loss='mse', optimizer='adam') return model # 生成音乐 def generate_music(model, pitch_to_int, int_to_pitch, seed, num_notes): predicted_notes = [] for i in range(num_notes): x = np.reshape(seed, (1, len(seed), 3)) prediction = model.predict(x, verbose=0) predicted_pitch = int_to_pitch[np.argmax(prediction[0][0:12])] predicted_duration = np.argmax(prediction[0][12:20]) predicted_quarter_length = np.argmax(prediction[0][20:]) + 1 predicted_notes.append([predicted_pitch, predicted_duration, predicted_quarter_length]) seed = np.concatenate([seed[1:, :], np.reshape(prediction, (1, 1, 3))], axis=0) return predicted_notes # 使用 data_dir = 'music.csv' notes = load_data(data_dir) # 编码和分类（调用的函数被省略了） pitch_to_int = {} int_to_pitch = {} num_pitches = 12 model = build_model(sequence_length, num_units) ``` 这个程序展示了如何利用LSTM模型来生成复调音乐。程序利用一个两层的LSTM模型来学习并生成新的复调音乐片段。你可以修改序列长度和LSTM的单元数来调整程序的表现。