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Many application studies rely on audio DNN models pre-trained on alarge-scale dataset as essential feature extractors, and they extract featuresfrom the last layers. In this study, we focus on our finding that the middlelayer features of existing supervised pre-trained models are more effectivethan the late layer features for some tasks. We propose a simple approach tocompose features effective for general-purpose applications, consisting of twosteps: (1) calculating feature vectors along the time frame from middle/latelayer outputs, and (2) fusing them. This approach improves the utility offrequency and channel information in downstream processes, and combines theeffectiveness of middle and late layer features for different tasks. As aresult, the feature vectors become effective for general purposes. In theexperiments using VGGish, PANNs' CNN14, and AST on nine downstream tasks, wefirst show that each layer output of these models serves different tasks. Then,we demonstrate that the proposed approach significantly improves theirperformance and brings it to a level comparable to that of thestate-of-the-art. In particular, the performance of the non-semantic speech(NOSS) tasks greatly improves, especially on Speech commands V2 with VGGish of+77.1 (14.3% to 91.4%).

Composing General Audio Representation by Fusing Multilayer Features of a Pre-trained Model