Knock-Knock: Acoustic object recognition by using stacked denoising autoencoders
نویسندگان
چکیده
منابع مشابه
Knock-Knock: Acoustic object recognition by using stacked denoising autoencoders
This paper presents a successful application of deep learning for object recognition based on acoustic data. The shortcomings of previously employed approaches where handcrafted features describing the acoustic data are being used, include limiting the capability of the found representation to be widely applicable and facing the risk of capturing only insignificant characteristics for a task. I...
متن کاملMultimodal Stacked Denoising Autoencoders
We propose a Multimodal Stacked Denoising Autoencoder for learning a joint model of data that consists of multiple modalities. The model is used to extract a joint representation that fuses modalities together. We have found that this representation is useful for classification tasks. Our model is made up of layers of denoising autoencoders which are trained locally to denoise corrupted version...
متن کاملMarginalized Stacked Denoising Autoencoders
Stacked Denoising Autoencoders (SDAs) [4] have been used successfully in many learning scenarios and application domains. In short, denoising autoencoders (DAs) train one-layer neural networks to reconstruct input data from partial random corruption. The denoisers are then stacked into deep learning architectures where the weights are fine-tuned with back-propagation. Alternatively, the outputs...
متن کاملDecoding Stacked Denoising Autoencoders
Data representation in a stacked denoising autoencoder is investigated. Decoding is a simple technique for translating a stacked denoising autoencoder into a composition of denoising autoencoders in the ground space. In the infinitesimal limit, a composition of denoising autoencoders is reduced to a continuous denoising autoencoder, which is rich in analytic properties and geometric interpretat...
متن کاملMarginalizing stacked linear denoising autoencoders
Stacked denoising autoencoders (SDAs) have been successfully used to learn new representations for domain adaptation. They have attained record accuracy on standard benchmark tasks of sentiment analysis across different text domains. SDAs learn robust data representations by reconstruction, recovering original features from data that are artificially corrupted with noise. In this paper, we prop...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neurocomputing
سال: 2017
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2017.03.014