# Audio Processing AGI Component

class AuditoryProcessing:
    def __init__(self):
        self.sampling_rate = 44100  # Standard CD quality sampling rate in Hz
        self.frames_per_window = 256  # Number of audio samples per window (FFT size)
        self.hop_length = 128         # Hop length between windows
        self.window_function = 'hann'  # Window function for FFT

    def preprocess_audio(self, raw_audio):
        """
        Preprocess the raw audio by applying a window and performing FFT.
        
        :param raw_audio: Raw audio signal as a list of samples
        :return: Complex FFT coefficients
        """
        import numpy as np
        from scipy.signal import get_window

        # Apply window function to the raw audio data
        window = get_window(self.window_function, self.frames_per_window)
        windowed_audio = raw_audio * window
        
        # Perform Fast Fourier Transform (FFT)
        fft_coeffs = np.fft.rfft(windowed_audio)
        
        return fft_coeffs

    def feature_extraction(self, fft_coeffs):
        """
        Extract features from the FFT coefficients.
        
        :param fft_coeffs: Complex FFT coefficients
        :return: A dictionary of extracted features
        """
        import numpy as np
        
        # Magnitude spectrum
        magnitude_spectrum = np.abs(fft_coeffs)
        
        # Power spectral density (PSD) for frequency domain analysis
        psd = np.square(magnitude_spectrum)
        
        # Mel-frequency cepstral coefficients (MFCCs)
        from librosa.feature import mfcc
        mfcc_features = mfcc(y=raw_audio, sr=self.sampling_rate)
        
        return {'magnitude_spectrum': magnitude_spectrum,
                'psd': psd,
                'mfcc_features': mfcc_features}

    def identify_speech(self, mfcc_features):
        """
        Identify speech in the audio using MFCCs.
        
        :param mfcc_features: Features extracted from FFT coefficients
        :return: Boolean indicating presence of speech
        """
        # Assuming a simple heuristic for now:
        # If any frame has at least one strong frequency component, it's likely speech
        threshold = 0.5
        for feature in mfcc_features:
            if np.max(feature) > threshold:
                return True
        return False

    def recognize_speech(self, mfcc_features):
        """
        Recognize spoken words using a pre-trained model.
        
        :param mfcc_features: Features extracted from FFT coefficients
        :return: String of recognized speech or None if not recognized
        """
        # Placeholder for actual recognition logic
        import librosa
        
        # Use librosa's built-in function to recognize speech
        transcription = librosa.effects.percussive(raw_audio)
        
        return transcription

    def process_audio(self, raw_audio):
        """
        Process the audio signal and return relevant information.
        
        :param raw_audio: Raw audio signal as a list of samples
        :return: A dictionary containing processed data
        """
        fft_coeffs = self.preprocess_audio(raw_audio)
        features = self.feature_extraction(fft_coeffs)
        speech_detected = self.identify_speech(features['mfcc_features'])
        transcription = None  # Placeholder for actual recognition
        
        if speech_detected:
            transcription = self.recognize_speech(features['mfcc_features'])
        
        return {'raw_audio': raw_audio,
                'fft_coeffs': fft_coeffs,
                'features': features,
                'speech_detected': speech_detected,
                'transcription': transcription}

# Example usage
if __name__ == "__main__":
    # Placeholder for a sample audio file path
    audio_file_path = "path/to/sample.wav"
    
    from scipy.io import wavfile
    sampling_rate, raw_audio = wavfile.read(audio_file_path)
    
    ap = AuditoryProcessing()
    processed_data = ap.process_audio(raw_audio)
    
    print(processed_data)

# The above code is a basic framework and would require more sophisticated implementation,
# including machine learning models for speech recognition.