What encoding to use for my musical vectors?
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I'm trying to build a music recommendations system using an encoder-decoder sequence-to-sequence architecture using keras. My dataset comprises of playlists containing songs represented as a 13-dimensional feature vector(beat,tempo,key etc). Each playlist acts as a training sample with song vectors for each time step (analogous to words in a sentence). At each time step of the decoder a song from the song vocabulary must be outputed.
The model: Encoder(input layer, single LSTM layer), Decoder(input layer,LSTM layer, softmax layer)
if S[0...N] is a playlist of songs:
encoder inputs = S[0...N-1],
decoder inputs = S[1...N],
decoder targets = decoder inputs shifted by one time step
I am presently using a one hot encoding on the song vocabulary to encode the songs. However this is becoming computationally expensive as the song vocabulary is huge (30000 songs). Furthermore, this limits the network to only learn from context of songs in playlist rather than the feature vectors along with context.
What alternative can I use for the one hot encoding? Is it possible to use the normalized feature vectors as is? If so how would my output layer change and what would be the loss function? Thanks.
machine-learning python keras recurrent-neural-net sequence-to-sequence
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FusRhoDa4897FusRhoDa4897
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I'm trying to build a music recommendations system using an encoder-decoder sequence-to-sequence architecture using keras. My dataset comprises of playlists containing songs represented as a 13-dimensional feature vector(beat,tempo,key etc). Each playlist acts as a training sample with song vectors for each time step (analogous to words in a sentence). At each time step of the decoder a song from the song vocabulary must be outputed.
The model: Encoder(input layer, single LSTM layer), Decoder(input layer,LSTM layer, softmax layer)
if S[0...N] is a playlist of songs:
encoder inputs = S[0...N-1],
decoder inputs = S[1...N],
decoder targets = decoder inputs shifted by one time step
I am presently using a one hot encoding on the song vocabulary to encode the songs. However this is becoming computationally expensive as the song vocabulary is huge (30000 songs). Furthermore, this limits the network to only learn from context of songs in playlist rather than the feature vectors along with context.
What alternative can I use for the one hot encoding? Is it possible to use the normalized feature vectors as is? If so how would my output layer change and what would be the loss function? Thanks.
machine-learning python keras recurrent-neural-net sequence-to-sequence
asked 1 min ago
FusRhoDa4897FusRhoDa4897
1
New contributor
FusRhoDa4897 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
I'm trying to build a music recommendations system using an encoder-decoder sequence-to-sequence architecture using keras. My dataset comprises of playlists containing songs represented as a 13-dimensional feature vector(beat,tempo,key etc). Each playlist acts as a training sample with song vectors for each time step (analogous to words in a sentence). At each time step of the decoder a song from the song vocabulary must be outputed.
The model: Encoder(input layer, single LSTM layer), Decoder(input layer,LSTM layer, softmax layer)
if S[0...N] is a playlist of songs:
encoder inputs = S[0...N-1],
decoder inputs = S[1...N],
decoder targets = decoder inputs shifted by one time step
I am presently using a one hot encoding on the song vocabulary to encode the songs. However this is becoming computationally expensive as the song vocabulary is huge (30000 songs). Furthermore, this limits the network to only learn from context of songs in playlist rather than the feature vectors along with context.
What alternative can I use for the one hot encoding? Is it possible to use the normalized feature vectors as is? If so how would my output layer change and what would be the loss function? Thanks.
machine-learning python keras recurrent-neural-net sequence-to-sequence
asked 1 min ago
FusRhoDa4897FusRhoDa4897
1
New contributor
FusRhoDa4897 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm trying to build a music recommendations system using an encoder-decoder sequence-to-sequence architecture using keras. My dataset comprises of playlists containing songs represented as a 13-dimensional feature vector(beat,tempo,key etc). Each playlist acts as a training sample with song vectors for each time step (analogous to words in a sentence). At each time step of the decoder a song from the song vocabulary must be outputed.
The model: Encoder(input layer, single LSTM layer), Decoder(input layer,LSTM layer, softmax layer)
if S[0...N] is a playlist of songs:
encoder inputs = S[0...N-1],
decoder inputs = S[1...N],
decoder targets = decoder inputs shifted by one time step
I am presently using a one hot encoding on the song vocabulary to encode the songs. However this is becoming computationally expensive as the song vocabulary is huge (30000 songs). Furthermore, this limits the network to only learn from context of songs in playlist rather than the feature vectors along with context.
What alternative can I use for the one hot encoding? Is it possible to use the normalized feature vectors as is? If so how would my output layer change and what would be the loss function? Thanks.
machine-learning python keras recurrent-neural-net sequence-to-sequence
machine-learning python keras recurrent-neural-net sequence-to-sequence
asked 1 min ago
FusRhoDa4897FusRhoDa4897
1
New contributor
FusRhoDa4897 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 min ago
FusRhoDa4897FusRhoDa4897
1
New contributor
FusRhoDa4897 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 min ago
FusRhoDa4897FusRhoDa4897
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FusRhoDa4897 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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asked 1 min ago
FusRhoDa4897FusRhoDa4897
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asked 1 min ago
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