StanislavKo28/music_moods_classification

Music mood classification is a fundamental and versatile application in many various domains. Some possible use cases for music mood classification include:

• music recommendation systems;
• content organization and discovery;
• radio broadcasting and programming;
• music licensing and copyright management;
• music analysis and research;
• content tagging and metadata enrichment;
• audio identification and copyright protection;
• music production and creativity;
• healthcare and therapy;
• entertainment and gaming.

The model is trained based on publicly available dataset of labeled music data — HWNAS Dataset — that contains 6930 sample 30-second audio files evenly split among 14 moods:

• angry;
• dark;
• energetic;
• epic;
• euphoric;
• happy;
• mysterious;
• relaxing;
• romantic;
• sad;
• scary;
• glamorous;
• uplifting;
• sentimental.

Kaggle notebooks:

• Training - Kaggle notebook;
• Inference - Kaggle notebook.

Epoch	F1	Eval Loss	Eval Precision	Eval Recall	Eval ROC-AUC
1	0.04875475200887581	2.632540464401245	0.030544660371068687	0.1344	0.6071502456887531
2	0.12188463004979133	2.534595251083374	0.09945756128059641	0.2096	0.7306199790432439
3	0.12932654401628893	2.3776655197143555	0.10665909627539523	0.2368	0.7810705576029681
4	0.15439325535918352	2.2560176849365234	0.21572622598078656	0.2464	0.8006652336372222
5	0.15367126167478184	2.1784613132476807	0.23305094905094903	0.2496	0.8082513555319044
6	0.17184978487546942	2.1160242557525635	0.2052915619474477	0.2496	0.8182308683863416
7	0.21190310802380768	2.069568634033203	0.3076689660390585	0.2736	0.8201191204703708
8	0.20610249743456857	2.0387115478515625	0.3111820988009202	0.2688	0.8236533161925041
9	0.21395825947099917	2.0054073333740234	0.24277322822503128	0.2752	0.8259563342861382
10	0.231388645761297	1.9618613719940186	0.24266619727764255	0.28	0.8353918139721079
11	0.2551684734694829	1.9417527914047241	0.2712793843125309	0.2976	0.8357069507628714
12	0.23859203206336047	1.9467965364456177	0.24560306346260846	0.2864	0.8338472674725235
13	0.23211297220177476	1.9273489713668823	0.23304501433192865	0.2752	0.8381106641271369
14	0.22241628577553074	1.941692590713501	0.22966472829618598	0.272	0.8346930076748398
15	0.25609690566033366	1.9033373594284058	0.2576226397619441	0.2944	0.8394839561830068
16	0.24723728556544475	1.889068603515625	0.2479616274726007	0.2816	0.8410164948564808
17	0.2392290231170979	1.8930455446243286	0.2334521319638967	0.2752	0.8409307834060327
18	0.26044061453780687	1.8662617206573486	0.2587323654531444	0.2944	0.8451350890943117
19	0.25675920854176	1.8768343925476074	0.2526957131583157	0.2832	0.8423786926762681
20	0.25703465327414055	1.870908260345459	0.25570025365133286	0.2832	0.8437720096266398
21	0.2622051040648687	1.8693370819091797	0.26097345900635055	0.2896	0.8440420530281421
22	0.24880722264161773	1.8653582334518433	0.24699021100428387	0.2784	0.8439683504327168