From 0944c0d06158898a8f46e11e99529e785a381e5a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=90=D1=80=D1=82=D0=B5=D0=BC=D0=B8=D0=B9=20=D0=9C=D1=83?= =?UTF-8?q?=D1=80=D0=B0=D0=B2=D1=8C=D0=B5=D0=B2?= Date: Sun, 29 Jun 2025 07:23:10 +0000 Subject: [PATCH] =?UTF-8?q?=D0=97=D0=B0=D0=B3=D1=80=D1=83=D0=B7=D0=B8?= =?UTF-8?q?=D1=82=D1=8C=20=D1=84=D0=B0=D0=B9=D0=BB=D1=8B=20=D0=B2=20=C2=AB?= =?UTF-8?q?/=C2=BB?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- MUSIC.py | 412 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 412 insertions(+) create mode 100644 MUSIC.py diff --git a/MUSIC.py b/MUSIC.py new file mode 100644 index 0000000..51d395d --- /dev/null +++ b/MUSIC.py @@ -0,0 +1,412 @@ +import numpy as np +import matplotlib +import matplotlib.pyplot as plt +from matplotlib.animation import FuncAnimation +import matplotlib.patches as patches +import librosa +import random +import pandas as pd +import threading +import time +from dataclasses import dataclass +from scipy.signal import stft, butter, sosfiltfilt +from scipy.fft import rfft, rfftfreq + +matplotlib.use('TkAgg') + +# Константы +SOUND_SPEED = 343.2 # скорость звука (м/с) +MIC_DISTANCE = 0.06 # расстояние между микрофонами (м) +ROOM_WIDTH = 3.0 # ширина комнаты (м) +ROOM_HEIGHT = 2.0 # высота комнаты (м) +SAMPLE_RATE = 48000 # частота дискретизации (Гц) +CHUNK = 16384 # размер буфера +RMS_THRESHOLD = 0.05 # порог RMS для определения звука +FREQUENCY_RANGE = (500, 2000) # диапазон частот для поиска доминирующей частоты (Гц) +ANGLE_RESOLUTION = 1 # шаг угла для поиска (градусы) +SILENCE_TIMEOUT = 2.0 # время в секундах для сохранения последнего угла +PEAK_THRESHOLD = 0.5 # порог для проверки достоверности пика в MUSIC-спектре +MOVE_INTERVAL = 0.5 # интервал перемещения источника звука (с) + +@dataclass +class Microphone: + """Класс для хранения информации о микрофоне""" + x: float + y: float + +@dataclass +class SoundSource: + """Класс для хранения информации об источнике звука""" + x: float + y: float + +class MusicDirectionFinder: + def __init__(self, mic_distance: float, audio_file: str): + """Инициализация определителя направления с использованием MUSIC""" + self.mic_distance = mic_distance + self.mic1 = Microphone(x=-mic_distance / 2, y=0.0) + self.mic2 = Microphone(x=mic_distance / 2, y=0.0) + self.sound_source = self._generate_random_sound_source() + self.running = True + self.current_angle = 0.0 + self.sound_detected = False + self.last_sound_time = 0 + self.last_detected_angle = None + self.show_arrow = False + self.rms_left = 0.0 + self.rms_right = 0.0 + self.audio_data, self.sample_rate = self.load_audio(audio_file) + self.audio_index = 0 + self.noise_level = 0.001 # шум + self.results = [] + self.source_positions = [(self.sound_source.x, self.sound_source.y, 0.0)] + self.last_move_time = time.time() + print("MusicDirectionFinder инициализирован") + + def _generate_random_sound_source(self) -> SoundSource: + """Генерация положения источника звука с последовательным проходом углов от -90 до 90 градусов с шагом 20 градусов""" + if not hasattr(self, 'angle_ranges'): + self.angle_ranges = list(range(-90, 91, 20)) + self.current_range_idx = 0 + self.angle_count = 0 + + start_angle = self.angle_ranges[self.current_range_idx] + end_angle = start_angle + 20 if self.current_range_idx < len(self.angle_ranges) - 1 else 90 + + angle_deg = random.uniform(start_angle, end_angle) + angle_rad = np.radians(angle_deg) + + distance = 1.5 # расстояние + source_x = distance * np.sin(angle_rad) + source_y = distance * np.cos(angle_rad) + + if abs(source_x) > ROOM_WIDTH / 2: + scale = (ROOM_WIDTH / 2) / abs(source_x) + source_x *= scale + source_y *= scale + if source_y > ROOM_HEIGHT: + scale = ROOM_HEIGHT / source_y + source_x *= scale + source_y *= scale + if source_y < 0: + source_y = 0.0 + source_x = 0.0 + + print( + f"Генерация новой позиции: угол={angle_deg:.2f}°, x={source_x:.2f}, y={source_y:.2f}, расстояние={distance:.2f}, повторение {self.angle_count + 1}/10") + + self.angle_count += 1 + if self.angle_count >= 10: + self.angle_count = 0 + self.current_range_idx = (self.current_range_idx + 1) % len(self.angle_ranges) + + return SoundSource(x=source_x, y=source_y) + + def load_audio(self, filename: str) -> tuple: + """Загрузка аудиофайла""" + try: + audio_data, sample_rate = librosa.load(filename, sr=SAMPLE_RATE, mono=True) + print(f"Аудиофайл загружен: {filename}, длина: {len(audio_data)}") + return audio_data, sample_rate + except Exception as e: + raise ValueError(f"Ошибка загрузки аудиофайла: {e}") + + def calculate_distances(self) -> tuple: + """Расчет расстояний от источника звука до микрофонов""" + l1 = np.sqrt((self.sound_source.x - self.mic1.x) ** 2 + + (self.sound_source.y - self.mic1.y) ** 2) + l2 = np.sqrt((self.sound_source.x - self.mic2.x) ** 2 + + (self.sound_source.y - self.mic2.y) ** 2) + return l1, l2 + + def process_signals_with_delay(self, signal: np.ndarray) -> tuple: + """Обработка сигналов с учетом временного сдвига и шума""" + l1, l2 = self.calculate_distances() + t1 = l1 / SOUND_SPEED + t2 = l2 / SOUND_SPEED + log = int(self.sample_rate * (t2 - t1)) + + if len(signal) < CHUNK: + signal = np.pad(signal, (0, CHUNK - len(signal)), mode='constant') + elif len(signal) > CHUNK: + signal = signal[:CHUNK] + + if log >= 0: + S1 = signal[abs(log):] if log != 0 else signal.copy() + S2 = signal[:-abs(log)] if log != 0 else signal.copy() + else: + S1 = signal[:-abs(log)] if log != 0 else signal.copy() + S2 = signal[abs(log):] + + min_length = min(len(S1), len(S2)) + S1 = S1[:min_length] + S2 = S2[:min_length] + + if len(S1) < CHUNK: + S1 = np.pad(S1, (0, CHUNK - len(S1)), mode='constant') + S2 = np.pad(S2, (0, CHUNK - len(S2)), mode='constant') + + noise1 = np.random.normal(0, self.noise_level, size=S1.shape) + noise2 = np.random.normal(0, self.noise_level, size=S2.shape) + S1 = S1 + noise1 + S2 = S2 + noise2 + + return S1, S2, t1, t2 + + def capture_audio(self): + """Эмуляция захвата аудиоданных из файла""" + if self.audio_index + CHUNK >= len(self.audio_data): + self.audio_index = 0 + chunk = self.audio_data[self.audio_index:self.audio_index + CHUNK] + self.audio_index += CHUNK + + signal1, signal2, t1, t2 = self.process_signals_with_delay(chunk) + print(f"capture_audio: длина signal1={len(signal1)}, signal2={len(signal2)}") + return signal1, signal2, t1, t2 + + def calculate_rms(self, signal: np.ndarray) -> float: + """Вычисление RMS сигнала""" + return np.sqrt(np.mean(signal ** 2)) + + def find_dominant_frequency(self, signal: np.ndarray) -> float: + """Определение доминирующей частоты сигнала через FFT""" + if len(signal) != CHUNK: + if len(signal) < CHUNK: + signal = np.pad(signal, (0, CHUNK - len(signal)), mode='constant') + else: + signal = signal[:CHUNK] + + fft_signal = np.abs(rfft(signal)) + freqs = rfftfreq(CHUNK, 1 / SAMPLE_RATE) + + if len(fft_signal) != len(freqs): + raise ValueError(f"Несоответствие размеров fft_signal ({len(fft_signal)}) и freqs ({len(freqs)})") + + mask = (freqs >= FREQUENCY_RANGE[0]) & (freqs <= FREQUENCY_RANGE[1]) + fft_signal = fft_signal[mask] + freqs = freqs[mask] + + if len(fft_signal) == 0: + print("find_dominant_frequency: нет частот в диапазоне, возвращаем FREQUENCY_RANGE[0]") + return FREQUENCY_RANGE[0] + dominant_freq = freqs[np.argmax(fft_signal)] + print(f"find_dominant_frequency: доминирующая частота = {dominant_freq:.1f} Hz") + return dominant_freq + + def bandpass_filter(self, signal: np.ndarray, freq: float) -> np.ndarray: + """Применение полосового фильтра к сигналу""" + bandwidth = 100 + lowcut = max(FREQUENCY_RANGE[0], freq - bandwidth / 2) + highcut = min(FREQUENCY_RANGE[1], freq + bandwidth / 2) + sos = butter(4, [lowcut, highcut], btype='band', fs=SAMPLE_RATE, output='sos') + filtered_signal = sosfiltfilt(sos, signal) + return filtered_signal + + def steering_vector(self, angle: float, freq: float) -> np.ndarray: + """Вычисление steering vector для заданного угла и частоты""" + tau = (self.mic_distance * np.sin(np.radians(angle))) / SOUND_SPEED + phase_shift = 2 * np.pi * freq * tau + return np.array([1, np.exp(1j * phase_shift)]) # Исправлено: -1j на 1j + + def music_spectrum(self, signal1: np.ndarray, signal2: np.ndarray, freq: float) -> tuple: + """Вычисление спектра MUSIC для оценки DoA""" + f, t, Zxx1 = stft(signal1, fs=SAMPLE_RATE, nperseg=CHUNK, noverlap=int(CHUNK * 3 / 4)) + _, _, Zxx2 = stft(signal2, fs=SAMPLE_RATE, nperseg=CHUNK, noverlap=int(CHUNK * 3 / 4)) + freq_idx = np.argmin(np.abs(f - freq)) + X = np.vstack((Zxx1[freq_idx, :], Zxx2[freq_idx, :])) + R = np.dot(X, X.conj().T) / X.shape[1] + R += np.eye(R.shape[0]) * 1e-6 + eigenvalues, eigenvectors = np.linalg.eigh(R) + idx = np.argsort(eigenvalues)[::-1] + eigenvectors = eigenvectors[:, idx] + En = eigenvectors[:, -1:] + angles = np.arange(-90, 91, ANGLE_RESOLUTION) + music_spectrum = np.zeros(len(angles)) + for i, angle in enumerate(angles): + a = self.steering_vector(angle, freq) + music_spectrum[i] = 1 / np.abs(np.dot(a.conj().T, np.dot(En @ En.conj().T, a))) + music_spectrum = music_spectrum / np.max(music_spectrum) + max_idx = np.argmax(music_spectrum) + print(f"music_spectrum: максимум спектра при угле {angles[max_idx]:.1f}°, значение {music_spectrum[max_idx]:.4f}") + if music_spectrum[max_idx] < PEAK_THRESHOLD: + print("music_spectrum: пик ниже порога, возвращаем 0.0") + return 0.0, angles, music_spectrum + estimated_angle = angles[max_idx] + print(f"music_spectrum: расчётный угол = {estimated_angle:.1f}°") + return estimated_angle, angles, music_spectrum + + def run(self): + """Обработка аудио в реальном времени""" + print("Запуск потока run") + while self.running: + try: + current_time = time.time() + if current_time - self.last_move_time >= MOVE_INTERVAL: + self.sound_source = self._generate_random_sound_source() + self.source_positions.append((self.sound_source.x, self.sound_source.y, current_time)) + print(f"Источник звука перемещен в: x={self.sound_source.x:.2f}, y={self.sound_source.y:.2f}") + self.last_move_time = current_time + + left, right, t1, t2 = self.capture_audio() + self.rms_left = self.calculate_rms(left) + self.rms_right = self.calculate_rms(right) + signal_diff = np.mean(np.abs(left - right)) + new_sound_detected = (self.rms_left > RMS_THRESHOLD) or (self.rms_right > RMS_THRESHOLD) + + if new_sound_detected: + print(f"RMS left: {self.rms_left:.4f}, RMS right: {self.rms_right:.4f}") + print(f"Signal difference: {signal_diff:.4f}") + freq = self.find_dominant_frequency(left) + left_filtered = self.bandpass_filter(left, freq) + right_filtered = self.bandpass_filter(right, freq) + angle, _, _ = self.music_spectrum(left_filtered, right_filtered, freq) + self.current_angle = angle + self.last_detected_angle = angle + self.last_sound_time = current_time + self.sound_detected = True + self.show_arrow = True + + true_dx = self.sound_source.x + true_dy = self.sound_source.y + true_angle = np.arctan2(true_dx, true_dy) * 180 / np.pi + self.results.append({ + 't1 (ms)': t1 * 1000, + 't2 (ms)': t2 * 1000, + 'Time Delay (ms)': (t2 - t1) * 1000, + 'Detected Angle (°)': angle, + 'True Angle (°)': true_angle, + 'Source X': self.sound_source.x, + 'Source Y': self.sound_source.y + }) + else: + if self.last_detected_angle is not None and current_time - self.last_sound_time < SILENCE_TIMEOUT: + self.current_angle = self.last_detected_angle + self.sound_detected = False + self.show_arrow = True + else: + self.sound_detected = False + self.show_arrow = False + time.sleep(CHUNK / self.sample_rate) + except Exception as e: + print(f"Ошибка в run: {e}") + continue + print("Поток run завершён") + + def get_coordinates_dataframe(self): + """Создание датафрейма с координатами микрофонов и всех позиций источника""" + data = { + 'Object': ['Mic1', 'Mic2'] + [f'SoundSource_{i}' for i in range(len(self.source_positions))], + 'X': [self.mic1.x, self.mic2.x] + [pos[0] for pos in self.source_positions], + 'Y': [self.mic1.y, self.mic2.y] + [pos[1] for pos in self.source_positions], + 'Time': [0.0, 0.0] + [pos[2] for pos in self.source_positions] + } + return pd.DataFrame(data) + + def get_results_dataframe(self): + """Создание датафрейма с результатами""" + return pd.DataFrame(self.results) + +def main(): + try: + finder = MusicDirectionFinder(MIC_DISTANCE, "my_recording1.wav") + print("Аудиофайл загружен, частота дискретизации:", finder.sample_rate) + + thread = threading.Thread(target=finder.run) + thread.start() + print("Поток run запущен") + + fig, ax = plt.subplots(figsize=(10, 8)) + ax.set_xlim(-ROOM_WIDTH / 2, ROOM_WIDTH / 2) + ax.set_ylim(-0.5, ROOM_HEIGHT) + ax.set_title("Определение направления на источник звука (MUSIC)") + ax.set_xlabel("X (м)") + ax.set_ylabel("Y (м)") + ax.grid(True) + print("График инициализирован") + + ax.plot(finder.mic1.x, finder.mic1.y, 'bs', markersize=10, label='Микрофон 1') + ax.plot(finder.mic2.x, finder.mic2.y, 'bs', markersize=10, label='Микрофон 2') + source_plot, = ax.plot(finder.sound_source.x, finder.sound_source.y, 'ro', markersize=10, + label='Источник звука') + + arrow_length = min(ROOM_WIDTH, ROOM_HEIGHT) / 3 + arrow = ax.arrow(0, 0, 0, 0, head_width=0.05, head_length=0.1, fc='r', ec='r', + label='Расчетное направление') + arrow_true = ax.arrow(0, 0, 0, 0, head_width=0.05, head_length=0.1, fc='g', ec='g', + linestyle=':', label='Истинное направление') + + sound_bar = ax.axhline(y=ROOM_HEIGHT - 0.2, color='green', linewidth=20, visible=False) + angle_text = ax.text(0, ROOM_HEIGHT - 0.3, "", ha='center', va='center', fontsize=12) + + left_indicator = patches.Rectangle((finder.mic1.x - 0.02, -0.1), 0.04, 0.05, facecolor='gray') + right_indicator = patches.Rectangle((finder.mic2.x - 0.02, -0.1), 0.04, 0.05, facecolor='gray') + ax.add_patch(left_indicator) + ax.add_patch(right_indicator) + + def update(frame): + try: + source_plot.set_data([finder.sound_source.x], [finder.sound_source.y]) + + dx = finder.sound_source.x + dy = finder.sound_source.y + true_angle_rad = np.arctan2(dx, dy) + true_end_x = arrow_length * np.sin(true_angle_rad) + true_end_y = arrow_length * np.cos(true_angle_rad) + arrow_true.set_data(x=0, y=0, dx=true_end_x, dy=true_end_y) + + if finder.show_arrow: + angle = finder.current_angle + calc_angle_rad = np.radians(angle) + calc_end_x = arrow_length * np.sin(calc_angle_rad) + calc_end_y = arrow_length * np.cos(calc_angle_rad) + arrow.set_data(x=0, y=0, dx=calc_end_x, dy=calc_end_y) + angle_text.set_text(f"Расчетный угол: {angle:.1f}°\nИстинный угол: {np.degrees(true_angle_rad):.1f}°") + + if finder.sound_detected: + sound_bar.set_visible(True) + ax.set_title("Активное обнаружение звука (MUSIC)") + else: + sound_bar.set_visible(False) + ax.set_title("Последнее зафиксированное направление (MUSIC)") + else: + arrow.set_data(x=0, y=0, dx=0, dy=0) + angle_text.set_text("") + sound_bar.set_visible(False) + ax.set_title("Звук не обнаружен") + + left_indicator.set_facecolor('green' if finder.rms_left > RMS_THRESHOLD else 'gray') + right_indicator.set_facecolor('green' if finder.rms_right > RMS_THRESHOLD else 'gray') + + return [source_plot, arrow, arrow_true, sound_bar, left_indicator, right_indicator, angle_text] + except Exception as e: + print(f"Ошибка в update: {e}") + return [] + + ani = FuncAnimation(fig, update, frames=None, interval=100, blit=True, cache_frame_data=False) + print("FuncAnimation инициализирован") + plt.legend(loc='upper right') + print("Вызов plt.show()") + plt.show() + print("plt.show() выполнен") + + finder.running = False + thread.join() + print("Поток run остановлен") + + coords_df = finder.get_coordinates_dataframe() + results_df = finder.get_results_dataframe() + print("\nКоординаты микрофонов и всех позиций источника звука:") + print(coords_df.to_string(index=False)) + print("\nРезультаты вычислений:") + print(results_df.to_string(index=False)) + coords_df.to_csv('coordinates_music.csv', index=False) + results_df.to_csv('results_music.csv', index=False) + print("\nДанные сохранены в 'coordinates_music.csv' и 'results_music.csv'") + + except Exception as e: + print(f"Ошибка в main: {e}") + finder.running = False + thread.join() + +if __name__ == "__main__": + main() \ No newline at end of file