Convolutional neural networks for music classification
E786388
"Convolutional neural networks for music classification" is a research work by Sander Dieleman that applies deep convolutional neural network architectures to automatically analyze and categorize music audio.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Convolutional neural networks for music classification canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T9245169 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Convolutional neural networks for music classification Context triple: [Sander Dieleman, notableWork, Convolutional neural networks for music classification]
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A.
Neural Filters
Neural Filters are Adobe Photoshop’s AI-powered tools that apply advanced, machine-learning-based adjustments and creative effects to images with minimal manual editing.
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B.
Connectionist Temporal Classification
Connectionist Temporal Classification is a neural network training algorithm designed for sequence labeling tasks where input and output lengths differ and alignments are unknown, widely used in speech and handwriting recognition.
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C.
ImageNet Classification with Deep Convolutional Neural Networks
"ImageNet Classification with Deep Convolutional Neural Networks" is the landmark 2012 research paper that introduced the deep CNN model AlexNet, demonstrating a dramatic leap in image recognition performance on the ImageNet benchmark and catalyzing the modern deep learning revolution in computer vision.
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D.
WaveNet
WaveNet is a deep generative neural network architecture for raw audio that produces highly natural-sounding speech and other audio signals.
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E.
SoundAnalysis framework
SoundAnalysis framework is an Apple framework that enables on-device audio classification and sound recognition using machine learning models.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Convolutional neural networks for music classification Target entity description: "Convolutional neural networks for music classification" is a research work by Sander Dieleman that applies deep convolutional neural network architectures to automatically analyze and categorize music audio.
-
A.
Neural Filters
Neural Filters are Adobe Photoshop’s AI-powered tools that apply advanced, machine-learning-based adjustments and creative effects to images with minimal manual editing.
-
B.
Connectionist Temporal Classification
Connectionist Temporal Classification is a neural network training algorithm designed for sequence labeling tasks where input and output lengths differ and alignments are unknown, widely used in speech and handwriting recognition.
-
C.
ImageNet Classification with Deep Convolutional Neural Networks
"ImageNet Classification with Deep Convolutional Neural Networks" is the landmark 2012 research paper that introduced the deep CNN model AlexNet, demonstrating a dramatic leap in image recognition performance on the ImageNet benchmark and catalyzing the modern deep learning revolution in computer vision.
-
D.
WaveNet
WaveNet is a deep generative neural network architecture for raw audio that produces highly natural-sounding speech and other audio signals.
-
E.
SoundAnalysis framework
SoundAnalysis framework is an Apple framework that enables on-device audio classification and sound recognition using machine learning models.
- F. None of above. chosen
Statements (28)
| Predicate | Object |
|---|---|
| instanceOf |
music information retrieval research
ⓘ
research work ⓘ scientific paper ⓘ |
| appliesTo | music audio analysis ⓘ |
| author | Sander Dieleman NERFINISHED ⓘ |
| comparesWith | hand-crafted audio features ⓘ |
| demonstrates | effectiveness of deep convolutional architectures for music tasks ⓘ |
| field |
audio signal processing
ⓘ
deep learning ⓘ machine learning ⓘ music information retrieval ⓘ |
| focusesOn |
automatic genre classification
ⓘ
automatic music tagging ⓘ music classification ⓘ supervised learning ⓘ |
| goal |
automatic analysis of music audio
ⓘ
automatic categorization of music audio ⓘ |
| hasAuthor | Sander Dieleman NERFINISHED ⓘ |
| inputRepresentation |
spectrograms
ⓘ
time–frequency representations of audio ⓘ |
| inputType | music audio ⓘ |
| language | English ⓘ |
| relatedTo |
audio content-based retrieval
ⓘ
automatic playlist generation ⓘ music recommendation systems ⓘ |
| topic |
end-to-end learning for music classification
ⓘ
representation learning from raw or minimally processed audio ⓘ |
| usesMethod | convolutional neural networks ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Convolutional neural networks for music classification Description of subject: "Convolutional neural networks for music classification" is a research work by Sander Dieleman that applies deep convolutional neural network architectures to automatically analyze and categorize music audio.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.