I am working on Airfoil Simulation data. I trained a multioutput regression model using Encode and Decoder and got MSE 8.78. I need help with what else the Model should use. My main target is that it takes less time for training.
Thank You.
I'm looking to train a word2vec 2.0 model from scratch, but I am a bit new to the field. Crucially, I would like to train it using a large dataset of non-human speech (i.e. cetacean sounds) in order to capture the underlying structure.
Once the pre-training is performed, is it possible to visualize the embeddings the model creates, in a similar way to how latent features are visualized in image processing when using e.g. CNNs? Or are the representations too abstract to be mapped to a spectrogram?
What I would like to do is to see what features the network is learning as the units of speech.
Thanks in advance for the help!
I am experimenting with object segmentation(round shaped objects that are often occur close together). I have used UNET deep neural network architecture for segmentation and obtained segmentation masks. I saved those in npy format.
I am a beginner in this area. I would like to know the ideal steps that I should follow now, if I want to apply watershed on the predicted masks with the aim of separating the objects.
I guess I need to convert the binary mask predicted to some form so that I can obtain some kind of markers indicating centroids.
Please help
I'm very new in deep learning, and I'm targeting to use GAN (Generative Adversarial Network) to recognize emotional speech. I've only known images being as inputs to most deep learning algorithms, such as GAN. but I'm curious as to how audio data can be an input into it, besides of using images of the spectrograms as the input. also, i'd appreciate it if you can explain it in laymen terms.
Audio data can be be represented in form of numpy arrays but before moving to that you must understand what audio really is. If you give a thought on what an audio looks like, it is nothing but a wave like format of data, where the amplitude of audio change with respect to time.
Assuming that our audio is represented in time domain, we can extract the values at every half-second(arbitrary). This is called sampling rate.
Converting the data into frequency domain can reduce the amount of computation requires as the sampling rate is less.
Now, let's load the data. We'll use a library called librosa , which can be installed using pip.
data, sampling_rate = librosa.load('audio.wav')
Now, you have both the data and the sampling rate. We can plot the waveform now.
librosa.display.waveplot(data, sr=sampling_rate)
Now, you have the audio data in form of numpy array. You can now study the features of the data and extract the ones you find interesting to train your models.
Further to Ayush’s discussion, for information on the challenges and work arounds of dealing with large amounts of data at different time scales in audio data I suggest this post on WaveNet: https://deepmind.com/blog/article/wavenet-generative-model-raw-audio
After that it sounds like you want to do classification. In that case a GAN on it’s own is not suitable. If you have plenty of data you could use a straight LSTM (or another type of RNN) which is designed to model time series, or you can take set sized chunks of input and use a 1-d CNN (similar to WaveNet). If you have lots of unlabelled data from the same or similar domain and limited training data you could use a GAN to learn to generate new samples, then use the discriminator from the GAN as pre-trained weights for a CNN classifier.
Since you are trying to perform Speech Emotion Recognition (SER) using deep learning, you can go for a recurrent architecture (LSTM or GRU) or a combination of CNN and recurrent network architecture (CRNN) instead of GANs since GANs are complicated and difficult to train.
In a CRNN, the CNN layers will extract features of varying details and complexity, whereas the recurrent layers will take care of the temporal dependencies. You can then finally use a fully connected layer for regression or classification output, depending on whether your output label is discrete (for categorical emotions like angry, sad, neutral etc) or continuous (arousal and valence space).
Regarding the choice of input, you can use either a spectrogram input (2D) or raw speech signal (1D) as input. For spectrogram input, you have to use a 2D CNN whereas for a raw speech signal you can use a 1D CNN. Mel scale spectrograms are usually preferred over linear spectrograms since our ears hear frequencies in log scale and not linearly.
I have used a CRNN architecture to estimate the level of verbal conflict arising from conversational speech. Even though it is not SER, it is a very similar task.
You can find more details in the paper
http://www.eecs.qmul.ac.uk/~andrea/papers/2019_SPL_ConflictNET_Rajan_Brutti_Cavallaro.pdf
Also, check my github code for the same paper
https://github.com/smartcameras/ConflictNET
and a SER paper whose code I reproduced in Python
https://github.com/vandana-rajan/1D-Speech-Emotion-Recognition
And finally as Ayush mentioned, Librosa is one of the best Python libraries for audio processing. You have functions to create spectrograms in Librosa.
Recently, I have learned decoder-encoder network and attention mechanism, and found that many papers and blogs implement attention mechanism on RNN network.
I am interested if other networks can incorporate attentional mechanisms.For example, the encoder is a feedforward neural network and decoder is an RNN. Can feedforward neural networks without time series use attentional mechanisms? If you can, please give me some suggestions.Thank you in advance!
In general Feed forward networks treat features as independent; convolutional networks focus on relative location and proximity; RNNs and LSTMs have memory limitations and tend to read in one direction.
In contrast to these, attention and the transformer can grab context about a word from distant parts of a sentence, both earlier and later than the word appears, in order to encode information to help us understand the word and its role in the system called sentence.
There is a good model for feed-forward network with attention mechanism here:
https://arxiv.org/pdf/1512.08756.pdf
hope to be useful.
Yes it is possible to use attention / self- attention / multi-head attention mechanisms to other feed forward networks. It is also possible to use attention mechanisms with CNN based architectures i.e which part of images should be paid more attention while predicting another part of an image. The mail idea behind attention is giving weight to all the other inputs while predicting a particular output or how we correlate words in a sentence for a NLP problem . You can read about the really famous Transformer architecture which is based on self-attention and has no RNN in it.
For getting a gist of different type of attention mechanism you can read this blog.