Kedis ku xidhan hagaha · Dhexdhexaad Heerka

Kediska Kala soocida Muuqaalka Muuqaalka

Sahami aqoonsiga muuqaalka jawiga, qalabka iswaafajinta, sifooyinka log-mel, kordhinta qalabka, iyo caqabadaha xusuusta ee moodooyinka qalabka.

Wadooyinka haga ee la xidhiidhaKala soocida Muuqaal Acoustic
Su'aal 1 ee 8

Sidee bay u kala soocida muuqaalka codku kaga duwan tahay ogaanshaha dhacdada dhawaaqa?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Sidee bay u kala soocida muuqaalka codku kaga duwan tahay ogaanshaha dhacdada dhawaaqa?
    • Dhab ahaantii waa isku hawl
    • ASC waxay aqoonsataa deegaanka guud, halka ogaanshaha dhacdadu ay aragto dhawaaqyo gaar ah
    • ASC waxay ka shaqeysaa muusigga oo kaliya
    • Ogaanshaha dhacdada ayaa iska indha tirta waqtiga gabi ahaanba
  2. Waa maxay dhibatada 'qalabka' ee ASC?
    • Ku hadlaayasha codka aad u dheer
    • Faylasha lagu kaydiyay qaab khaldan
    • Qaab lagu tabobaray hal makarafoon oo si liidata u shaqaynaya mid kale
    • Laba muuqaal oo isu eg
  3. Waa kuwee matalaad gelinta ah ee caadiga ah moodooyinka ASC?
    • Qoraallada qoraalka ceeriin
    • Sawirada JPEG ee goobta
    • Isku-duwayaasha GPS
    • Log-mel spectrograms
  4. Waa maxay sababta moodooyinka ASC ee barkada ay u muuqdaan ilbiriqsiyo badan halkii ay ka ahaan lahaayeen hal daqiiqado?
    • Sababtoo ah muuqaallada waxaa lagu qeexay tirakoobka dhawaaqa muddada-dheer, ee ma aha dhacdo keli ah
    • Si loo yareeyo cabbirka faylka
    • Sababtoo ah makarafoonadu waa gaabis
    • Si meesha looga saaro dhammaan qaylada
  5. Caqabaddee cilmi baadhiseed ayaa keentay inta badan horumarka ASC?
    • Tartanka ImageNet
    • Abaalmarinta Loebner
    • Imtixaanka Turing
    • Caqabadda DCASE
  6. What inference-computation budget did the DCASE 2024 low-complexity acoustic-scene task specify for a one-second audio clip?
    • Exactly 30 billion operations for every device
    • At most 30 million multiply-accumulate operations
    • No computation limit of any kind
    • A limit measured only by the recording filename length
  7. How can quantization help an acoustic-scene model meet a parameter-storage budget?
    • By storing weights with fewer bits per parameter
    • By adding more high-precision copies of every weight
    • By replacing the memory budget with an accuracy score
    • By making the microphone sample rate unlimited
  8. What variation is device-aware augmentation intended to simulate for acoustic-scene training?
    • Only changes in the spelling of scene labels
    • Only a different album-cover image
    • Changes in recording-device characteristics such as microphone frequency response
    • A guarantee that all environments have identical acoustics