對於聽力受損的人，助聽器已經成為他們生活中不可或缺的必需品，在助聽器中，環境噪音分類和估計是重要的技術，然而有些環境噪音分類器使用多種音頻特徵當作分類器的輸入特徵，導致環境噪音分類器計算量增加，除此之外，環境噪音分類器如果採用不同的時間長度作為輸入特徵，可能會影響噪音分類的效果。因此，本論文提出了一種用於環境噪音分類的深度學習模型，並對三種不同長度的聲音片段進行實驗，本文所提出的模型擁有更少的浮點數運算及參數，並只使用對數縮放的梅爾譜圖作為輸入特徵。我們採用UrbanSound8K資料集和HANS資料集對提出的模型進行分類準確度、模型計算複雜度、模型參數和模型推理時間的評估。實驗結果表明，我們提出的模型在兩個資料集上優於其他模型，與其他模型相比，所提出的模型在保持分類準確度的同時降低了模型複雜度和推理時間。因此，本文提出針對助聽器的噪音分類器在計算複雜度降低的情況下並不會影響噪音分類器的性能。

Hearing aids are increasingly essential for people with hearing loss. For this purpose, environmental noise estimation and classification are some of the required technologies. However, some noise classifiers utilize multiple audio features, which cause intense computation. In addition, such noise classifiers employ inputs of different time lengths, which may affect classification performance. Thus, this thesis proposes a model architecture for noise classification, and performs experiments with three different audio segment time lengths. The proposed model attains fewer floating-point operations and parameters by utilizing the log-scaled mel-spectrogram as an input feature. The proposed models are evaluated with classification accuracy, computational complexity, trainable parameters, and inference time on the UrbanSound8K dataset and HANS dataset. The experimental results showed that the proposed model outperforms other models on two datasets. Furthermore, compared with other models, the proposed model reduces model complexity and inference time while maintaining classification accuracy. As a result, the proposed noise classification for hearing aids offers less computational complexity without compromising performance.

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