Kediska Kala Soocida Nooca Muusiga

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Waa maxay mel-spectrogram, oo inta badan loo isticmaalo qaybinta nooc-kala soocida shabakadaha neerfaha?
   • Sawirka wakhtiga-soo noqnoqda ee maqalka iyadoo la isticmaalayo cabirka garaadka garashada
   • Liiska heesaha heesaha oo lambarro loo beddelay
   • Qaab mowjad ah oo muujinaya baaxad weyn oo cayriin ah oo ka dambeeya wakhtiga oo keliya
   • Kaydka magacyada fanaaniinta iyo sannadaha la sii daayo
2. Waa kuwee kaydka xogta caadiga ah ee ka kooban 1,000 sodon iyo ilbiriqsi clips oo ka kooban 10 nooc?
   • ImageNet
   • GTZAN
   • Hadal-qoraal
   • COCO
3. Waa maxay sababta ay u adag tahay in la gaaro saxnaanta kala-soocidda nooca saxda ah?
   • Kumbuyuutarku gabi ahaanba ma farsamayn karaan maqalka
   • Dhammaan heesaha waxay u dhawaaqaan isku mid algorithms
   • Xuduudaha noocu waa kuwa daahsoon iyo dhaqan ahaan
   • Galalka maqalku aad bay u yar yihiin in la falanqeeyo
4. Qaabkee gacan-ku-samaynta ah ayaa si weyn loogu isticmaalay nidaamyada kala-soocidda nooca hore?
   • saamiga JPEG cadaadis
   • HTML tag inta jeer
   • Sawirrada dhalaalka Pixel
   • Isku-duwayaasha xuubka-soo-celinta bowdada (MFCCs)
5. Muxuu soo saaraa lakabka softmax ee dhamaadka kala soocida nooca?
   • Qaybinta itimaalka ee fasalada nooca
   • Qaab hal mawjad oo maqal ah
   • Fayl MP3 ah oo la cufan
   • Sawir spectrogram ah
6. How can self-supervised music pretraining reduce dependence on labeled genre examples?
   • It eliminates the need to evaluate genre labels
   • It can learn audio representations from unlabeled music, which are then evaluated or fine-tuned with labeled examples
   • It guarantees correct labels for every song
   • It converts genre categories into universal facts
7. How can exact or repeated excerpts in GTZAN distort a random train/test evaluation?
   • They ensure that every genre label is objective
   • They make the test set contain more genres than training
   • If repetitions cross the split, a model may see duplicate or near-duplicate material in both sets
   • They remove any need to inspect class labels
8. How do MFCCs differ from a mel-spectrogram representation?
   • MFCCs are the model’s final genre probabilities
   • A mel-spectrogram removes the time axis entirely
   • MFCCs measure only the recording’s file size
   • MFCCs summarize the log-mel spectrum with cepstral coefficients