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The development of audio event recognition systems require labeled trainingdata, which are generally hard to obtain. One promising source of recordings ofaudio events is the large amount of multimedia data on the web. In particular,if the audio content analysis must itself be performed on web audio, it isimportant to train the recognizers themselves from such data. Training fromthese web data, however, poses several challenges, the most important being theavailability of labels: labels, if any, that may be obtained for the data aregenerally weak, and not of the kind conventionally required for trainingdetectors or classifiers. We propose that learning algorithms that can exploitweak labels offer an effective method to learn from web data. We then propose arobust and efficient deep convolutional neural network (CNN) based framework tolearn audio event recognizers from weakly labeled data. The proposed method cantrain from and analyze recordings of variable length in an efficient manner andoutperforms a network trained with strongly labeled web data by a considerablemargin. Moreover, even though we learn from weakly labeled data, where eventtime stamps within the recording are not available during training, ourproposed framework is able to localize events during the inference stage.

Deep CNN Framework for Audio Event Recognition using Weakly Labeled Web Data