class AuditoryProcessingComponent:
    def __init__(self):
        # Initialize parameters for auditory processing
        self.sampling_rate = 44100  # Standard audio sampling rate (Hz)
        self.fft_size = 2048        # FFT size for frequency analysis
        self.window_size = 512      # Window size for the window function
        self.overlap = 0.75         # Overlap between consecutive windows

        # Frequency bins and time resolution based on FFT size and sampling rate
        self.freq_bins = self.fft_size // 2 + 1
        self.time_resolution = (self.window_size * self.overlap) / self.sampling_rate

        # Initialize buffers for audio processing
        self.audio_buffer = []
        self.current_window = []

    def preprocess_audio(self, raw_audio):
        """Preprocess the raw audio signal before analysis."""
        # Apply windowing and normalization to the input audio
        windowed_audio = self.apply_window(raw_audio)
        normalized_audio = self.normalize(windowed_audio)

        return normalized_audio

    def apply_window(self, audio_data):
        """Apply a Hanning window to the audio data."""
        self.current_window = [0.5 * (1 - math.cos(2 * math.pi * i / (self.window_size - 1))) for i in range(self.window_size)]
        return [a * b for a, b in zip(audio_data[:self.window_size], self.current_window)]

    def normalize(self, windowed_audio):
        """Normalize the windowed audio data."""
        max_val = max(windowed_audio)
        if max_val > 0:
            return [val / max_val for val in windowed_audio]
        else:
            return windowed_audio

    def perform_fft(self, normalized_audio):
        """Perform Fast Fourier Transform on the preprocessed audio."""
        fft_result = np.fft.rfft(normalized_audio, self.fft_size)
        magnitude_spectrum = 2.0 / self.fft_size * abs(fft_result)[:-1]
        return magnitude_spectrum

    def extract_features(self, magnitude_spectrum):
        """Extract relevant features from the magnitude spectrum."""
        # Example: Energy and Spectral Centroid
        energy = sum(magnitude_spectrum)
        spectral_centroid = np.sum([i * val for i, val in enumerate(magnitude_spectrum)]) / energy
        return {'energy': energy, 'spectral_centroid': spectral_centroid}

    def process_audio_segment(self, raw_audio):
        """Process a segment of audio and extract relevant features."""
        normalized_audio = self.preprocess_audio(raw_audio)
        magnitude_spectrum = self.perform_fft(normalized_audio)
        features = self.extract_features(magnitude_spectrum)

        return features

    # Example method to handle overlapping windows
    def process_overlapping_windows(self, raw_audio):
        """Process overlapping audio segments for continuous analysis."""
        window_size = 512
        step_size = int(window_size * (1 - self.overlap))
        total_length = len(raw_audio)
        features_list = []

        start = 0
        while start < total_length:
            segment = raw_audio[start:start + window_size]
            if len(segment) == window_size:  # Ensure the segment is of the correct length
                processed_features = self.process_audio_segment(segment)
                features_list.append(processed_features)

            start += step_size

        return features_list