Commit 23ea89c2 authored by Olivier REYNET's avatar Olivier REYNET
Browse files

Correction --> au_reading.h

parent e71761fb
......@@ -110,3 +110,6 @@ class CartTree:
return self.right._predict(xfeature)
else:
return self.label
#TODO GENERATE C++ CODE TO TAKE THE DECISION !
#TODO TO GENERATE, OVERLOAD __str__ in this class and use a prefix walk of the tree.
\ No newline at end of file
#ifndef AU_READING_H
#define AU_READING_H
#include "globals.h"
//TODO USE THESE HEADERS ELEMENTS TO READ .AU FILES
//32 bit word (unsigned) field Description/Content Hexadecimal numbers in C notation
//0 magic number the value 0x2e736e64 (four ASCII characters ".snd")
//1 data offset the offset to the data in bytes, must be divisible by 8. The minimum valid number is 24 (decimal), since this is the header length (six 32-bit words) with no space reserved for extra information (the annotation field). The minimum valid number with an annotation field present is 32 (decimal).
......@@ -35,112 +37,9 @@
//5 channels the number of interleaved channels, e.g., 1 for mono, 2 for stereo; more channels possible, but may not be supported by all readers.
#include <iostream>
#include <fstream>
#include <cstdio>
#include <cmath>
#include <string>
#include <iostream>
#include <filesystem>
#include <vector>
#include "globals.h"
namespace fs = std::filesystem;
struct AU_header {
uint32_t magic_number;
uint32_t data_offset;
uint32_t data_size;
uint32_t encoding;
uint32_t sample_rate;
uint32_t channels;
};
std::ostream &operator<<(std::ostream &s, const AU_header *h) {
return s << "AU Header:\n"
<< std::hex << "\tMagic Number:\t 0x" << std::hex << h->magic_number << "\n"
<< std::dec << "\tData Offset:\t " << std::dec << h->data_offset << "\n"
<< std::dec << "\tData size:\t " << (double) (h->data_size) / (1.0 * (1 << 20)) << " MiB\n"
<< std::dec << "\tEncoding:\t " << h->encoding << "\n"
<< std::dec << "\tSample Rate:\t " << h->sample_rate << " sample/s \n"
<< std::dec << "\tChannels:\t " << h->channels
<< std::dec;
}
uint32_t read_w(std::ifstream &file, bool bigEndian=true) {
uint32_t word = 0;
uint8_t b;
for (std::size_t i = 0; i < 4; i++) {
file.read(reinterpret_cast<char *>(&b), sizeof(uint8_t));
if (bigEndian)
word = word | (b << ((3 - i) * 8));
else
word = word | (b << (i * 8));
}
return word;
}
void auFileDetails(const std::string fileName) {
FILE *fin = fopen(fileName.c_str(), "rb");
std::ifstream myFile(fileName);
std::unique_ptr<AU_header> header = std::make_unique<AU_header>();
header->magic_number = read_w(myFile);
std::cout << "(0-3) Magic number: " << std::hex << header->magic_number << std::endl;
header->data_offset = read_w(myFile);
std::cout << "(4-7) Data Offset: " << std::dec << header->data_offset << std::endl;
header->data_size = read_w(myFile);
std::cout << "(8-11) Data size: "
<< header->data_size
<< " B, "
<< header->data_size / (1.0 * (1 << 20))
<< " MiB"
<< std::endl;
header->encoding = read_w(myFile);
std::cout << "(12-15) Encoding: " << header->encoding << std::endl;
header->sample_rate = read_w(myFile);
std::cout << "(16-19) Sample rate: " << header->sample_rate << std::endl;
header->channels = read_w(myFile);
std::cout << "(20-23) Channels: " << header->channels << std::endl;
fclose(fin);
}
DataVector readAuFile(const std::string fileName) {
FILE *fin = fopen(fileName.c_str(), "rb");
std::ifstream myFile(fileName);
//TODO
DataVector data;
std::unique_ptr<AU_header> header = std::make_unique<AU_header>();
header->magic_number = read_w(myFile);
//std::cout << "(0-3) Magic number: " << std::hex << header->magic_number << std::endl;
header->data_offset = read_w(myFile);
//std::cout << "(4-7) Data Offset: " << std::dec << header->data_offset << std::endl;
header->data_size = read_w(myFile);
//std::cout << "(8-11) Data size: "
// << header->data_size
// << " B, "
// << header->data_size / (1.0 * (1 << 20))
// << " MiB"
// << std::endl;
header->encoding = read_w(myFile);
//std::cout << "(12-15) Encoding: " << header->encoding << std::endl;
header->sample_rate = read_w(myFile);
//std::cout << "(16-19) Sample rate: " << header->sample_rate << std::endl;
header->channels = read_w(myFile);
//std::cout << "(20-23) Channels: " << header->channels << std::endl;
myFile.seekg(header->data_offset, std::ios_base::beg);
uint8_t lower_bits;
uint8_t higher_bits;
for (std::size_t k = 0; k < header->data_size/2; k++) {
myFile.read(reinterpret_cast<char *>(&higher_bits), sizeof(uint8_t));
myFile.read(reinterpret_cast<char *>(&lower_bits), sizeof(uint8_t));
data.push_back(static_cast<real>((signed short) ((higher_bits << 8) + lower_bits)));
}
myFile.close();
fclose(fin);
return data;
}
......
......@@ -59,7 +59,6 @@ void ite_dit_fft(std::vector<Complex> &x) {
std::size_t step = stages - stage;
std::size_t halfSize = currentSize / 2;
for (std::size_t k = 0; k < problemSize; k = k + currentSize) {
//for (std::size_t k = 0; k <= problemSize - currentSize; k = k + currentSize) {
for (std::size_t j = 0; j < halfSize; j++) {
auto u = x[k + j];
auto v = x[k + j + halfSize] * tf[j * (1 << step)];
......
[[_TOC_]]
# Embedded Machine Learning
Ce projet GitLab est la base du code que vous devez rendre sur Moodle. Il faut en respecter l'esprit et l'organisation.
[[_TOC_]]
# Embarquer la prédiction issue de l'apprentissage automatique ?
......@@ -59,7 +58,7 @@ Les styles musicaux sont :
Ce projet utilise nécessairement :
- un compilateur C++ à la norme 20,
- cmake (>=3.18),
- cmake (>=3.16),
- Python 3.9 et Scikit Learn.
CMake est utilisé pour construire les exécutables du projet. L'utilisation d'un compilateur local, d'un cross-compilateur ou d'un compilateur distant (sur la cible) est à paramétrer dans les préférences de CLion directement (Build > Toolchains).
......@@ -85,18 +84,18 @@ Il sera possible d'ajouter à ces éléments TensorFlow Lite et d'autres outils
DATASETS contient les ensembles de données utilisées pour apprendre et tester les algorithmes implémentés.
Pour le dépôt Git, on n'a pas gardé tous les extraits musicaux, afin de na pas encombrer inutilement les disques.
[À vous de télécharger les données complètes ici.](http://marsyas.info/downloads/datasets.html)
[À vous de télécharger les données complètes ici au format AU.](http://marsyas.info/downloads/datasets.html)
## Helpers
Helpers contient un ensemble de fonctionnalités utiles au développement :
- Lire des fichiers .wav ou .au (au_reading.h, wav_reading.h),
- Afficher des vecteurs ou des tableaux facilement (print_helpers.h),
- Sélectionner des fichiers pour l'entraînement et le test et en garder la trace (file_helpers),
- Calculer une transformée de Fourier rapide (signal.h),
- Manipuler facilement des styles sous la forme d'un énuméré plutôt que des entiers (music_style_helpers).
- Définir des types ad-hoc pour le signal et les features (etypes.h) et avoir la possibilité de compiler rapidement en simple ou en double précision,
- Définir des variables globales (globals.h).
- **au_reading.h** et **wav_reading.h** Lire des fichiers .wav ou .au ,
- **print_helpers.h** Afficher des vecteurs ou des tableaux facilement ,
- **file_helpers.h** Sélectionner des fichiers pour l'entraînement et le test et en garder la trace,
- **signal.h** Calculer une transformée de Fourier rapide,
- **music_style_helpers.h** Manipuler facilement des styles sous la forme d'un énuméré plutôt que des entiers.
- **etypes.h** Définir des types ad-hoc pour le signal (DataVector) et les features et avoir la possibilité de compiler rapidement en simple ou en double précision.
- **globals.h** Définir des variables globales.
## Extraction
......@@ -104,7 +103,7 @@ Helpers contient un ensemble de fonctionnalités utiles au développement :
Ce répertoire contient un cadre de code pour l'extraction de paramètres.
Celle-ci est nécessaire sur votre machine et sur la cible : en effet, pour prédire une classe d'appartenance, le système embarqué doit être capable de générer les paramètres.
Tel quel, l'exécutable de ce répertoire produit un fichier de paramètres au format CSV (dans cmake-build-debug/Extraction) formaté avec entêtes.
Tel quel, l'exécutable de ce répertoire produit un fichier de paramètres au format CSV (dans cmake-build-debug/Extraction) formaté avec entêtes. **Il est donc inutile de modifier les sections qui ne comportent pas de //TODO**. Ces sections sont repérables facilement sous CLion (en vert clair dans le code ou en liseret dans l'ascenceur).
Chaque colonne est nommée et le nom désigne un paramètre particulier :
- BINAVG3 est la moyenne du bin 3 de la STFT partiquée sur un échantillon de musique.
......@@ -145,11 +144,12 @@ Ils seront automatiquement inscrits dans le fichier généré par write_csv.
2. sans utiliser de bibliothèques externes
3. sur votre machine et sur la cible embarquée (dans notre cas Raspberry Pi 4).
2. (obligatoire) Veiller à donner la preuve que le code fonctionne sur la cible.
3. (obligatoire) Calculer et mesurer les complexités temporelle et mémoire de vos algorithmes.
4. (obligatoire) Vérifier la performance de chaque classificateur embarqué sur les fichiers de test sélectionnés avant l'entraînement.
3. (obligatoire) Évaluer (et mesurer) les complexités temporelle et mémoire de vos algorithmes.
4. (obligatoire) Vérifier la performance de chaque classificateur embarqué sur les fichiers de test sélectionnés avant l'entraînement afin de les comparer.
5. Programmer une extraction de paramètres en C++ performante :
1. (obligatoire) efficace dans son exécution (1 Go de données musicales peuvent être traitées en 60 secondes ou moins),
2. minimale en termes de paramètres significatifs générés pour l'apprentissage :
1. (obligatoire) compléter le fichier au_reading.h afin de [convertir les fichiers AU du dataset en DataVector en C++](https://en.wikipedia.org/wiki/Au_file_format).
2. (obligatoire) efficace dans son exécution (1 Go de données musicales peuvent être traitées en 60 secondes ou moins),
3. minimale en termes de paramètres significatifs générés pour l'apprentissage :
1. (obligatoire) approche STFT
2. (facultatif) approche MFCC
6. Programmer des arbres de décision :
......
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