Auditory_perception_ears.c

#include "auditory_perception.h"

// Assuming we need the EARS library for audio data handling
#include "ears.h"

// Memory Management
int init_auditory_scene(AuditoryScene **scene) {
    *scene = (AuditoryScene*)malloc(sizeof(AuditoryScene));
    if (!*scene) {
        fprintf(stderr, "Failed to allocate memory for auditory scene\n");
        return EARS_MEMORY_ERROR;
    }
    (*scene)->num_perceptions = 0;
    return EARS_SUCCESS;
}

void free_auditory_scene(AuditoryScene *scene) {
    if (scene) {
        free(scene);
    }
}

// Processing and Perception
int process_audio_for_perception(AudioData *audio_data, AuditoryScene *scene, InnerEar *inner_ear) {
    if (!audio_data || !scene || !inner_ear) {
        fprintf(stderr, "Invalid input parameters\n");
        return EARS_MEMORY_ERROR;
    }
    
    // Perform Fourier Transform to analyze frequency content
    if (perform_fourier_transform(audio_data) != EARS_SUCCESS) {
        fprintf(stderr, "Failed to perform Fourier Transform\n");
        return EARS_MEMORY_ERROR;
    }
    
    // Find the dominant frequency
    double dominant_frequency = find_dominant_frequency(audio_data);
    if (dominant_frequency > 0) {
        snprintf(scene->perceptions[scene->num_perceptions].label, MAX_LABEL_LENGTH, "Tone at %.2f Hz", dominant_frequency);
        scene->perceptions[scene->num_perceptions].confidence = 0.8; // Arbitrary confidence value
        scene->num_perceptions++;
    }
    
    // Apply cochlear response simulation
    simulate_cochlear_response(inner_ear, dominant_frequency);
    
    // Here would be the integration with machine learning models or more complex analysis
    // For now, let's use placeholder functions:
    classify_sound(scene);
    identify_speaker(scene);
    
    // Add context based on some external data or scenario
    contextualize_sound(scene, "In a quiet room");
    
    return EARS_SUCCESS;
}

// Placeholder for actual frequency analysis
double find_dominant_frequency(AudioData *audio_data) {
    // This is a very simplified approach. In real scenarios, this would involve 
    // finding the peak in the frequency spectrum.
    double max_amplitude = 0.0;
    double dominant_freq = 0.0;
    for (int i = 0; i < FFT_SIZE / 2 + 1; i++) {
        double current_amplitude = sqrt(audio_data->frequency_domain[i][0] * audio_data->frequency_domain[i][0] + 
                                        audio_data->frequency_domain[i][1] * audio_data->frequency_domain[i][1]);
        if (current_amplitude > max_amplitude) {
            max_amplitude = current_amplitude;
            dominant_freq = (i * SAMPLING_RATE) / (double)FFT_SIZE;
        }
    }
    return dominant_freq;
}

// Placeholder for actual sound classification
void classify_sound(AuditoryScene *scene) {
    if (scene->num_perceptions < MAX_PERCEPTIONS) {
        strcpy(scene->perceptions[scene->num_perceptions].label, "Classified as Music");
        scene->perceptions[scene->num_perceptions].confidence = 0.75;
        scene->num_perceptions++;
    }
}

// Placeholder for actual speaker identification
void identify_speaker(AuditoryScene *scene) {
    if (scene->num_perceptions < MAX_PERCEPTIONS) {
        strcpy(scene->perceptions[scene->num_perceptions].label, "Speaker: John Doe");
        scene->perceptions[scene->num_perceptions].confidence = 0.9;
        scene->num_perceptions++;
    }
}

// Add context to the auditory scene
void contextualize_sound(AuditoryScene *scene, const char *context) {
    if (scene->num_perceptions < MAX_PERCEPTIONS) {
        snprintf(scene->perceptions[scene->num_perceptions].label, MAX_LABEL_LENGTH, "Context: %s", context);
        scene->perceptions[scene->num_perceptions].confidence = 1.0;
        scene->num_perceptions++;
    }
}