Auditory_perception.c

#include "auditory_perception.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;
    }
    
    // Here you would integrate with machine learning models or pattern recognition algorithms
    // For simplicity, let's simulate some basic perception:
    
    // Example: Detecting a tone
    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);
    
    // Simulate more complex perception like speech or music recognition here
    
    return EARS_SUCCESS;
}

// This function would use machine learning to classify sounds
void classify_sound(AuditoryScene *scene) {
    // Placeholder for actual classification logic
    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++;
    }
}

// This function would use voice recognition to identify speakers
void identify_speaker(AuditoryScene *scene) {
    // Placeholder for actual speaker identification logic
    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++;
    }
}

// This function would add context to the perception based on external data
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; // Context is assumed to be accurate
        scene->num_perceptions++;
    }
}

// Helper function, this would be implemented with signal processing techniques
double find_dominant_frequency(AudioData *audio_data) {
    // Placeholder for actual frequency analysis
    return 440.0; // Example: A4 note frequency
}