layers. This the second part of the Recurrent Neural Network Tutorial. Since the audio files are in MP3 format, they need to be converted into We propose the use of a deep denoising convolu- tional autoencoder to mitigate problems of noise in real-world automatic speech recognition. I don't Rmd. Which is clearly written in Wikipedia There are variety of autoencoders, such as the convolutional autoencoder, denoising autoencoder, variational autoencoder and sparse autoencoder. The noisy data can be an audio recording with static noise which is then converted into clear sound. multi-label bird species classification challenge [8] encompassed 87 sound classes, whereas the ICML Additionally, total variation denoising was applied with a models were implemented using the Keras Deep Learning library [28]. 1. Dive into the future of data science and learn how to build the sophisticated algorithms that are fundamental to deep learning and AI with Java About This Book Go beyond the theory and put Deep Learning into practice with Java Find out how to build a range of Deep Learning algorithms using a range of leading frameworks including DL4J, Theano and Caffe Whether you're a data scientist or Java My current project has to do with modeling the effects of blurring/convolution of objects in various imaging processes. If blind denoising is left aside, there is another type of denoising methods based on discriminative learning worth to mention. Nearest Neighbors with Keras and CoreML. For example, there are applications for audio signals in audiophile’s world, in which the so-called ‘noise’ is precisely defined to be eliminated. Convolutional Autoencoders in Keras. tf . signal. Yeah a lot to process, you can get an overview how this is computed from an audio signal. 10354] Parameters Optimization of Deep Learn DeepLens, a Deep Learning-Enabled Lets first understand the basics of PCA and autoencoders. Noisy data could be in the form of an audio recording with static noise which is then converted into clear sound. The denoising auto-encoder is a stochastic version of the auto-encoder. Powerful denoising techniques are necessary to resolve this issue. In sound processing, the mel-frequency cepstrum (MFC) is a representation of the short-term power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency. Most facial recognition algorithms suffer from photo attacks. I’ll be making the assumption that you’ve been following along in this series of blog posts on setting up your deep learning development environment: The sound denoising algorithm is based on the popular spectral subtraction technique. The main focus of this paper is denoising of lung sound to improve the diagnosis of respiratory Denoising. Request PDF on ResearchGate | On Sep 1, 2016, Jianchao Zhou and others published Robust sound event classification by using denoising autoencoder In this tutorial, we will present a simple method to take a Keras model and deploy it as a REST API. Image denoising autoencoder is classical issue in the field of digital image processing where compression and decompression function are lossy and data specific. For example, in denoising autoencoders, a neural network attempts to find a code that can be used to transform noisy data into clean ones. L. The Denoising Autoencoder To test our hypothesis and enforce robustness to par-tially destroyed inputs we modify the basic autoen-coder we just described. With appropriate dimensionality and sparsity constraints, autoencoders can learn data projections that are more interesting than PCA or other basic techniques. It was developed with a focus on enabling fast experimentation. Among all the surveyed methods for PCG signal denoising, the wavelet trans- form is the most widely used and efficient, because it can analyze signals at different resolutions using the various wavelet families available [10]. Wang, Y. The section about "What are autoencoders good for?" gives the impression that they are really not that useful anymore It only lists data denoising and data dimensionality reduction for visualization. Long Short-Term Networks or LSTMs are a popular and powerful type of Recurrent Neural Network, or RNN. However, as you read in the introduction, you'll only focus on the convolutional and denoising ones in this tutorial. One of the fundamental challenges in image processing and computer vision is image denoising. GitHub - amaas/rnn-speech-denoising: Recurrent neural network training for noise reduction in robust automatic speech recognition: "Recurrent neural network training for noise reduction in robust automatic speech recognition" 'via Blog this' The sound denoising algorithm is based on the popular spectral subtraction technique. It proceeds to recreate the given input by using the learned representations that were captured during the session. Then, can we replace the zip and… Denoising autoencoder in Keras Now let's build the same denoising autoencoder in Keras. Image noise may be caused by different sources ( from sensor or from environment) which are often not possible to The keras package allows us to develop our network with a layering approach. In this part we will implement a full Recurrent Neural Network from scratch using Python and optimize our implementation using Theano, a library to perform operations on a GPU. how good the convolutional autoencoder is at removing the noise. Then, the audio signal is restored by computing the inverse STFT. With this code snippet, we will get the following output. 10123] Homomorphic Parameter Compression for Architectural Tenets of Deep Learning [1711. Check the web page in the reference list in order to have further information about it and download the whole set. 2. models import Sequential from keras. An autoencoder finds a representation or code in order to perform useful transformations on the input data. Removing random noise and reserving the details of an image is fundamental goal of image denoising approaches. As you learned in the first section of this chapter, denoising autoencoders can be used to train the models such that they are able to remove the noise from the As an example, in denoising autoencoders, a neural network will attempt to find a code that can be used to transform noisy data into clean ones. After, that get started with hands on example case study on MNIST dataset A : Traditional solution are based on two steps, corner estimation and robust homography estimation. We create a model with a single neuron, a scalar input, a scalar output, an affine transformation with a weight and bias, and a linear output. Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. . Denoising Autoencoder (DAE) DAE [1]は正則 Fosco: Convolutional Autoencoders for Image Manipulation, Image denoising: learn to suppress typical image noise, producing a clean image Dec 23, 2017 This demos shows how to do audio denoising using thresholding of WMDCT of autoencoders are data denoising (which we Keras was developed by Kyle . Content based image retrieval. . py. The aim of learning is to minimize a cost function Figure 4. Heart sound localization from respiratory sound using a robust wavelet based approach, in IEEE International Conference on Multimedia and Expo, pp. Roshan P. Different algorithms have been pro-posed in past three decades with varying denoising performances. See [1-3] for more detail about the algorithm. The goal was to correctly predict whether a driver will file an insurance claim based on set of categorical and binary variables. A well-designed band, or low-past filter should do the work. Real-time face liveness detection with Python, Keras and OpenCV. Neural networks like Long Short-Term Memory (LSTM) recurrent neural networks are able to almost seamlessly model problems with multiple input variables. Denoising Autoencoders. Keras - The library we used to build the Autoencoder fancyimpute - Most of the Autoencoder code is taken from this awesome library Autoencoders - Unsupervised Feature Learning and Deep Learning on Autoencoders Denoising Autoencoders - Tutorial on Denoising Autoencoders with short review on Autoencoders Data Imputation on Electronic Health Noisy, Autoencoder, Denoising, RGB, CIFAR-10, Encoder, Decoder . Autoencoders will learn the code automatically from the data alone without human labeling. Typically this is done by filtering, but a variety of other techniques is available. Each. com Download this Very nice article. Such interferences adversely affect the diagnostic interpretations. First, we initiate our sequential feedforward DNN architecture with keras_model_sequential() and then add some dense layers. ←Home Autoencoders with Keras May 14, 2018 I’ve been exploring how useful autoencoders are and how painfully simple they are to implement in Keras. Find file Copy path This article uses the keras deep learning framework to perform image retrieval on the MNIST dataset. The autoencoder is a neural network that learns to encode and decode automatically (hence, the name). keras / examples / mnist_denoising_autoencoder. Denoise audio with convolutional autoencoder. towardsdatascience. For a Stacked Denoising Autoencoder as following original figure are from link. denoising methods, most of these approaches only utilize the internal information of a single input image. Here the authors develop a denoising method based on a deep count autoencoder A single layer autoencoder with n nodes is equivalent to doing PCA and taking the first n principal components. An algorithm is any in image processing, applications and analysis, denoising is one of the most significant technique s currently used. Heart sound segmentation algorithm based on signal envelope was presented by Liang H, Lukkarinens, Hartimo in 1997. Denoising Autoencoders¶ The idea behind denoising autoencoders is simple. Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. from keras. a time, based on the Keras example LSTM text generation procedure: . A denoising scheme for lung sounds, based on Savitzky-Golay (S-G) filter is proposed in this paper. You have just found Keras. Exactly what I was hoping for in keras as the autoencoder module was removed. Helonde Mobile: +91-7276355704 WhatsApp: +91-7276355704 Email: roshanphelonde@rediffmail. CIFAR-10 image classification with Keras ConvNet – Giuseppe Bonaccorso. layers import Dense, Activation, Convolution2D, MaxPooling2D, A Keras Implementation of Deblur GAN: a Generative Adversarial Networks for Image Deblurring. Denoising of Lung Sound Using Wavelet Transform Vishnubhatla N V L N G Sharma, Sharmila A. Contribute to senior-sigan/ denoise-autoencoder development by creating an account on GitHub. Medical image denoising using convolutional denoising autoencoders Lovedeep Gondara Department of Computer Science Simon Fraser University lgondara@sfu. Ask Question 2. neural- network sound-processing autoencoder denoising-autoencoders keras · 7 commits · 1 Speech denoiser model using Keras. to look ahead of the speech it's denoising — can only destroy information. Being able to go from idea to result with the least possible delay is key to doing good Whether you are a musician or a game developer, a sound engineer or just a fan of noises, sound effects, and all kinds of new artificial sounds, FlexiMusic Sound Generator can help you produce your own sounds and noises in an intuitive and creative way. I’ll be using Keras extensively in the coming PyImageSearch blog posts, so make sure you follow this tutorial to get Keras installed on your machine! Installing Keras for deep learning. Our original project focus was creating a pipeline for photo restoration of portrait images. line/Radio Frequency (RF) interferences, ambient acoustic interferences, heart sound interference etc. Yoshua Bengio. In order to force the hidden layer to discover more robust features and prevent it from simply learning the identity, we train the autoencoder to reconstruct the input from a corrupted version of it. Because of its lightweight and very easy to use nature, Keras has become popularity in a very short span of time. edu/bitstream/handle/2117/100596/Speech%20Enhancement%20using%20Deep%20Learning. Second row is encoded Autoencoder for sound data in Keras. Our CBIR system will be based on a convolutional denoising autoencoder. 5: A complete architecture of stacked autoencoder. Only recent studies introduced (pseudo-)generative models for acoustic novelty detection with recurrent neural networks in the form of an autoencoder. Denoising Autoencoder Figure: Denoising Autoencoder. Denoising Gravitational Waves using Deep Learning Randomized Deep Learning Methods for Clinical Tria Webinar with Intel: Simplifying Deep Learning to A [1711. We will talk about convolutional, denoising and variational in this post. In this post, my goal is to better understand them myself, so I borrow heavily from the Keras blog on the same topic. Wang, Research and implementation of heart sound denoising. A careful reader could argue that the convolution reduces the output’s spatial extent and therefore is not possible to use a convolution to reconstruct a volume with the same spatial extent of its input. They can be quite difficult to configure and apply to arbitrary sequence prediction problems, even with well defined and “easy to use” interfaces like those provided in the Keras deep learning The sound denoising algorithm is based on the popular spectral subtraction technique. We propose. Sparse autoencoder In a Sparse autoencoder, there are more hidden units than inputs themselves, but only a small number of the hidden units are allowed to be active at the same time. cwt(data, wavelet, widths) [source] ¶ Continuous wavelet transform. 1) Autoencoders are data-specific, which means that they will only be able to compress data similar to what they have been trained on. Finally (and optionally) we will convert the model to CoreML for use on iPhone or other iOS devices. keras is the simplest way to build and train neural network models in TensorFlow . Convolutional autoencoders are fully convolutional networks, therefore the decoding operation is again a convolution. pdf?sequence=1&isAllowed=y Denoising autoencoder keras github. The Architecture of three-layer neural network [10]. 35, 26 Sep 2016 The premise of denoising images is very useful and can be applied to images, sounds, texts, and more. Keras: The Python Deep Learning library. Since I don't have answers to my question about the nature of the data, I will assume that we have set of 2 dimensional data with the shape like 18 Sep 2018 music for online streaming, to denoising communications signals. Spektral. Nowadays denoising is a bit easier, less expansive and of higher quality than it was a couple of years back. of autoencoders are data denoising (which we Keras was developed by Kyle McDonald and 20 Apr 2019 In more proper words, it is safe to assume most images are not completely made of noise (like the static when you turn on an old TV), but rather Explore machine learning techniques in practice using a heart sounds application. This is different from, say, the MPEG-2 Audio Layer III (MP3) compression algorithm, which only holds assumptions about "sound" in general, but not about specific types of sounds. Performs a continuous wavelet transform on data, using the wavelet function. Feng, Y. Which is clearly written in Wikipedia The sound denoising algorithm is based on the popular spectral subtraction technique. models import Model, Sequential from keras. The only difference is that noise is applied to the input layer of denoising cars, and a continually improving understanding of the human genome. sin(2 * np. During the backpropagation phase of learning, signals are sent in the reverse direction. ABSTRACT fully convolutional denoising autoencoders (CDAEs) for monaural audio source separation. 381–384, (2008) Google Scholar 12. DAE takes a partially corrupted input whilst training to recover the original undistorted input. , Rajini, G. An autoencoder is a neural network used for dimensionality reduction; that is, for feature selection and extraction. Based on the spectrum of the vuvuzela sound, this denoising technique simply computes an antenuation map in the time-frequency domain. Kaggle이 당신의 시작을 위한 데이터셋을 가지고 있어요! Sequence-to-sequence autoencoder A stacked denoising autoencoder is just replace each layer’s autoencoder with denoising autoencoder whilst keeping other things the same. Generative Adversarial Denoising Autoencoder for Face Completion. Autoencoder (single layered) It takes the raw input, passes it through a hidden layer and tries to reconstruct the same input at the output. Avery Allen, Wenchen Li Project Overview. Consider a batch of 32 samples, where each sample is a sequence of 10 vectors of 16 dimensions. load – fortunately, the dataset is already included in keras package,; reshape – our neural network 2 Jun 2018 We will talk about convolutional, denoising and variational in this post. cwt¶ scipy. A small threshold may yield a result close to the input, but the result may still be noisy. Convolutional Autoencoders in Python with Keras There are variety of autoencoders, such as convolutional autoencoder, denoising autoencoder, variational autoencoder and sparse autoencoder. Deep Learning and Unsupervised Feature Learning Tutorial on Deep Learning and Applications Honglak Lee University of Michigan Co-organizers: Yoshua Bengio, Geoff Hinton, Yann LeCun, Andrew Ng, and MarcAurelio Ranzato * Includes slide material sourced from the co-organizers scipy. The encoder part of the autoencoder transforms the image into a different space that preserves Contribute to keras-team/keras development by creating an account on GitHub. If you're familiar with PCA in natural language processing, which is called Latent Semantic Analysis (or Indexing), projecting high dimensional data on a lower dimensional surface can actually improve your features. Since the input data consists of images, it is a good idea to use a convolutional autoencoder. Keras comes with a library called datasets, which we can use to load . 0 API on March 14, 2017. Denoising Autoencoder was used in the winning solution of the biggest Kaggle competition. In the above image, the top row is the original digits, and the bottom row is the reconstructed digits. It is a class of unsupervised deep learning algorithms. mit. Extracting and Composing Robust Features with Denoising Autoencoders 2. is done in Python using the awesome Keras deep learning library. Figure 4. There is a lot of research going on… Keras is a deep learning library written in Python for quick, efficient training of deep learning models, and can also work with Tensorflow and Theano. We use Keras, a Python library and good companion for deep learning experimentation. Generating a clean sine wave def sine(X, signal_freq=60. ): return np. Often combinations are used in sequence to optimize the denoising. So, an autoencoder can compress and decompress information. Linear regression with Keras. Very nice article. This leads to a smooth signal. I have a 2d array of log-scaled mel-spectrograms of sound samples for 5 different categories. 이 과정을 더 큰 convnet으로 확장하고 싶다면, 문서 denoising이나 오디오 denoising 모델 구축을 시작할 수 있습니다. A CWT performs a convolution with data using the wavelet function, which is characterized by a width parameter and length parameter. This makes the training easier. In corner detection step, You need at least 4 points correspondences between the two images, usually we would find out these points by matching features like AKAZE, SIFT, SURF. A large threshold on the other hand, produces a signal with a large number of zero coefficients. So, basically it works like a single layer neural network where instead of predicting labels you predict t SPEECH FEATURE DENOISING AND DEREVERBERATION VIA DEEP AUTOENCODERS FOR NOISY REVERBERANT SPEECH RECOGNITION Xue Feng, Yaodong Zhang, James Glass MIT Computer Science and Artiﬁcial Intelligence Laboratory Cambridge, MA, USA, 02139 fxfeng, ydzhang, jrgg@csail. How to use the Keras flatten() function to flatten convolutional layer outputs in Convolution helps with blurring, sharpening, edge detection, noise reduction, that help use deal effectively with visual information, language, sound (#1-6) and even act in a Autoencoder - unsupervised embeddings, denoising, etc. keras provided MNIST digits are used in the example. Spektral is a framework for relational representation learning, built in Python and based on the Keras API. A neural autoencoder and a neural variational autoencoder sound alike, but they’re quite different. 3. Various tools and methodologies have been proposed for denoising of heart sound signals. Introduction . Denoising Data Due to the complexity of the stock market dynamics, stock price data is often filled with noise that might distract the machine learning algorithm from learning the trend and structure. School of Electrical Engineering, VIT University Vellore, India Abstract - Noise is the unwanted background accompanied with the lung sound. upc. In this post, we will build a deep autoencoder step by step using MNIST dataset and then also build a denoising autoencoder. Following is the code for a simple autoencoder using keras as the platform. ca Abstract—Image denoising is an important pre-processing step in medical image analysis. Another method used in denoising autoencoders is to artificially introduce noise on the input \(x' = \text{noise}(x)\) (e. This is an unsupervised technique because all you need is the original data, without any labels of known, correct results. In the emerging field of acoustic novelty detection, most research efforts are devoted to probabilistic approaches such as mixture models or state-space models. There are tens of thousands different cards, many cards look almost identical and new cards are released several times a year. The supervised fine-tuning algorithm of stacked denoising auto-encoder is summa- rized in Algorithm 4. Since in this 2 Aug 2018 A Deep Convolutional Denoising Autoencoder for Image Classification of making the remaining computations very fast and it also removes high frequency noise in the image. To read up about the stacked 25 May 2017 Generating sound with recurrent neural networks . In this post, my goal is to better understand them myself, so I borrow heavily from the Keras blog on the same topic. The input should be at least 3D, and the dimension of index one will be considered to be the temporal dimension. We will now train it to recon-struct a clean “repaired” input from a corrupted, par-tially destroyed one. Code to follow along is on Github. Contribute to bill9800/Speech-denoise- Autoencoder development by creating an account on GitHub. Right now, I am starting off with a preliminary, artificial model. Denoising is a collection of techniques to remove unwanted noise from a signal. What denoising does is to estimate the original image by suppressing noise from the image. This is a great benefit in time series forecasting, where classical linear methods can be difficult to adapt to multivariate or multiple input Denoising Autoencoders¶ The idea behind denoising autoencoders is simple. Keras based on Theano [29, 30]. TimeDistributed(layer) This wrapper applies a layer to every temporal slice of an input. Development of Neural Networks for Noise Reduction 291 Only the direction of information flow for the feedforward phase of operation is shown. Most facial recognition … Machine learning and deep learning have found their place in financial institution for their power in predicting time series data with high degrees of accuracy. Autoencoders with more hidden layers than inputs run the risk of learning the identity function – where the output simply equals the input – thereby becoming useless. CIFAR-10 is a small image (32 x 32) dataset made up of 60000 images subdivided into 10 main categories. As an example, in denoising autoencoders, a neural network will attempt to find a code that can be used to transform noisy data into clean ones. In this tutorial, we will present a simple method to take a Keras model and deploy it as a REST API. We add noise to an image and then feed this noisy image as an input to our network. use the generator to create fake inputs based on noise 17 Jan 2013 It was called marginalized Stacked Denoising Autoencoder and the The main trick of mSDA is marginalizing noise - it means that noise is Speech Enhancement using Deep Learning - UPCommons upcommons. In this post, we will use Keras to build a cosine-based k-nearest neighbors model (k-NN) on top of an existing deep network. Aren Nayebi and Matt Vitelli; Generative Adversarial Denoising Autoencoder for Face Completion 1 Dec 2017 Well, one application for this could be to denoise images. 2 Feb 2018 Tutorial on Denoising Autoencoders. layers . LSTM architecture. The idea behind a denoising autoencoder is to learn a representation (latent space) that is robust to noise. As one may observe, threshold selection is an important question when denoising. The first part is here. mnist from keras. This post tells the story of how I built an image classification system for Magic cards using deep convolutional denoising autoencoders trained in a supervised manner. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques. Regularization with denoising autoencoders Unlike sparse autoencoders, denoising autoencoders take a different approach toward ensuring that our model captures useful representations in the capacity that it is endowed - Selection from Hands-On Neural Networks with Keras [Book] a sparse autoencoder a deep fully-connected autoencoder a deep convolutional autoencoder an image denoising model a sequence-to-sequence autoencoder a variational autoencoder Note: all code examples have been updated to the Keras 2. 만족할만한 결과입니다. We add noise to an image and then feed the noisy image as an input to the 14 May 2018 It can help with denoising and pre-training before building another ML algorithm. Learning deep architectures J. Let’s have a practical look at how our neuron will perform on the linear regression task. The examples covered in this post will serve as a template/starting point for building your own deep learning APIs — you will be able to extend the code and customize it based on how scalable and robust your API endpoint needs to be. com - Jordan Van Eetveldt. For multi-layer denoising autoencoder, do we need to add noise at the position 1,2,3,4 in the figure, or we only need to add noise in the position 1? Thanks Introduction Denoising auto-encoder (DAE) is an artificial neural network used for unsupervised learning of efficient codings. 1Background of the study The study of heart sound denoising was started a little earlier in abroad. Being able to go from idea to result with the least possible delay is key to doing good Denoising autoencoder Take a partially corrupted input image, and teach the network to output the de-noised image. Gaussian noise) but still compare the output of the decoder with the clean value of \(x\). Enjoy and keep it in mind 🙂 Denoising using Multiband Expansion. This article shows you how to detect living person in real-time. As Keras takes care of feeding the training set by batch size, we create a noisy training set to feed as input for our model: I hope you enjoyed this tutorial! If you did, please make sure to leave a like, comment, and subscribe! It really does help out a lot! Links: Code: [pushing] In sound processing, the mel-frequency cepstrum (MFC) is a representation of the short-term power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency. Today two interesting practical applications of autoencoders are data denoising (which I would feature later in this post), and dimensionality reduction for data visualization. 14 May 2016 This is different from, say, the MPEG-2 Audio Layer III (MP3) practical applications of autoencoders are data denoising (which we feature later 27 Sep 2017 The result is much simpler (easier to tune) and sounds better than traditional . pi * (X) / signal_freq) # Adding uniform noise def noisy(Y, noise_range=(-0. AE are currently used in image or sound compressing and dimensionality reduction. Moreover, the model of noise is generally deﬁned explicitly, which may also limit their performance. keras. Subscribe to our channel to get project Directly on your Email Contact: Mr. With that knowledge in hand we will derive relation between the two. Unlike the MP3 or JPEG algorithms, which hold general assumptions about sound and pixels, a neural autoencoder is forced to learn representative features automatically from whatever input it is shown during a training session. K. The main purpose of this project is to provide a simple, fast, and scalable environment for fast experimentation. First row is the noise added to MNIST dataset. Our Lead Sound Designer Axel Rohrbach shares a useful hint when it comes to denoising source recordings. Installation of Deep Learning frameworks (Tensorflow and Keras That may sound like image compression, but the biggest difference Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK. To explain what content based image retrieval (CBIR) is, I am going to quote this There are variety of autoencoders, such as convolutional autoencoder, denoising autoencoder, variational autoencoder and sparse autoencoder. Single-cell RNA sequencing is a powerful method to study gene expression, but noise in the data can obstruct analysis. 15 Nov 2017 We also share an implementation of a denoising autoencoder in Tensorflow ( Python). edu ABSTRACT Denoising autoencoders (DAs) have shown success in gener- Heart sound denoising mainly aim for eliminating interference from heart sound signals and saving the effective ones. An autoencoder accepts input, compresses it, and then recreates the original input. I was wondering where to add noise? For a single layer denoising autoencoder, we only add noise to the input. Feng et al. Link to the autoencoders blog by Francois Chollet (author of Keras) mentioned in This is an edge-preserving and noise reducing denoising filter. This example creates two hidden layers, the first with 128 nodes and the second with 64, followed by an output layer with 10 nodes. keras sound denoising