You cannot select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.

101 lines
3.0 KiB
C++

#include "fluid_client_nmf.h"
#include "STFT.hpp"
#include "RatioMask.hpp"
namespace fluid{
namespace nmf{
using fluid::nmf::NMF;
using fluid::stft::STFT;
using fluid::stft::ISTFT;
using fluid::stft::Spectrogram;
using fluid::FluidTensor;
NMFClient::NMFClient(size_t rank,size_t iterations, size_t fft_size, size_t window_size, size_t hop_size):
m_rank(rank),m_iterations(iterations), m_fft_size(fft_size), m_window_size(window_size), m_hop_size(hop_size), m_has_processed(false), m_has_resynthed(false)
{}
void NMFClient::process(const FluidTensor<double, 1> &data, bool resynthesise)
{
m_audio_buffers.resize(m_rank,data.extent(0));
m_has_processed = false;
m_has_resynthed = false;
STFT stft(m_window_size,m_fft_size,m_hop_size);
Spectrogram spec = stft.process(data);
FluidTensor<double, 2> mag = spec.getMagnitude();
NMF nmf(m_rank,m_iterations);
m_model = nmf.process(spec.getMagnitude());
m_has_processed = true;
if(resynthesise)
{
ratiomask::RatioMask mask(m_model.getMixEstimate(),1);
ISTFT istft(m_window_size, m_fft_size, m_hop_size);
for(int i = 0; i < m_rank; ++i)
{
RealMatrix estimate = m_model.getEstimate(i);
Spectrogram result(mask.process(spec.mData, estimate));
RealVector audio = istft.process(result);
m_audio_buffers.row(i) = audio;
}
m_has_resynthed = true;
}
}
size_t NMFClient::dictionary_size() const
{
return m_has_processed ? m_model.getW().extent(0) : 0 ;
}
size_t NMFClient::activations_length() const{
return m_has_processed ? m_model.getH().extent(1) : 0;
}
size_t NMFClient::num_sources() const
{
return m_has_resynthed ? m_audio_buffers.size() : 0;
}
const FluidTensorView<double, 1> NMFClient::dictionary(const size_t idx) const
{
assert(m_has_processed && idx < m_model.W.cols());
return m_model.getW().col(idx);
}
const FluidTensorView<double, 1> NMFClient::activation(const size_t idx) const
{
assert(m_has_processed && idx < m_model.H.rows());
return m_model.getH().row(idx);
}
const FluidTensor<double,2> NMFClient::dictionaries() const
{
return m_model.getW();
}
const FluidTensor<double,2> NMFClient::activations()const
{
return m_model.getH();
}
const FluidTensorView<double, 1> NMFClient::source(const size_t idx) const
{
assert(idx < m_audio_buffers.rows() && "Range Error");
return m_audio_buffers.row(idx);
}
// source_iterator NMFClient::sources_begin() const
// {
// return m_audio_buffers.cbegin();
// }
//
// source_iterator NMFClient::sources_end() const
// {
// return m_audio_buffers.cend();
// }
}//namespace nmf
}//namespace fluid