audioLIME: Listenable Explanations Using Source Separation
Deep neural networks (DNNs) are successfully applied in a wide variety of music information retrieval (MIR) tasks but their predictions are usually not interpretable. We propose audioLIME, a method based on Local Interpretable Model-agnostic Explanations (LIME) extended by a musical definition of locality. The perturbations used in LIME are created by switching on/off components extracted by source separation which makes our explanations listenable. We validate audioLIME on two different music tagging systems and show that it produces sensible explanations in situations where a competing method cannot.
Code (2)
Tasks
Information RetrievalMusic Information RetrievalMusic TaggingRetrievalMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Towards Musically Meaningful Explanations Using Source Separation
Deep neural networks (DNNs) are successfully applied in a wide variety of music information retrieval (MIR) tasks. Such models are usually considered "black boxes", meaning that their predictions are not interpretable. P…
Explainable ModelsImage SegmentationInformation RetrievalMusic Information Retrieval+2LMAC-TD: Producing Time Domain Explanations for Audio Classifiers
Neural networks are typically black-boxes that remain opaque with regards to their decision mechanisms. Several works in the literature have proposed post-hoc explanation methods to alleviate this issue. This paper propo…
DecoderTracing Back Music Emotion Predictions to Sound Sources and Intuitive Perceptual Qualities
Music emotion recognition is an important task in MIR (Music Information Retrieval) research. Owing to factors like the subjective nature of the task and the variation of emotional cues between musical genres, there are …
Emotion RecognitionInformation RetrievalMusic Emotion RecognitionMusic Information Retrieval+2AGRI-Fidelity: Evaluating the Reliability of Listenable Explanations for Poultry Disease Detection
Existing XAI metrics measure faithfulness for a single model, ignoring model multiplicity where near-optimal classifiers rely on different or spurious acoustic cues. In noisy farm environments, stationary artifacts such …
Listenable Maps for Zero-Shot Audio Classifiers
Interpreting the decisions of deep learning models, including audio classifiers, is crucial for ensuring the transparency and trustworthiness of this technology. In this paper, we introduce LMAC-ZS (Listenable Maps for A…
Decoderzero-shot-classificationZero-Shot Learning