void MultipannerNode::strategyChanged() {
int newStrategy = getProperty(Lav_PANNER_STRATEGY).getIntValue();
int newOutputSize=2;
bool hookHrtf = false, hookAmplitude = false;
switch(newStrategy) {
case Lav_PANNING_STRATEGY_HRTF:
hookHrtf = true;
break;
case Lav_PANNING_STRATEGY_STEREO:
std::dynamic_pointer_cast<AmplitudePannerNode>(amplitude_panner)->configureStandardChannelMap(2);
hookAmplitude = true;
break;
case Lav_PANNING_STRATEGY_SURROUND40:
std::dynamic_pointer_cast<AmplitudePannerNode>(amplitude_panner)->configureStandardChannelMap(4);
hookAmplitude = true;
newOutputSize = 4;
break;
case Lav_PANNING_STRATEGY_SURROUND51:
std::dynamic_pointer_cast<AmplitudePannerNode>(amplitude_panner)->configureStandardChannelMap(6);
hookAmplitude = true;
newOutputSize=6;
break;
case Lav_PANNING_STRATEGY_SURROUND71:
std::dynamic_pointer_cast<AmplitudePannerNode>(amplitude_panner)->configureStandardChannelMap(8);
hookAmplitude=true;
newOutputSize=8;
break;
}
if(hookAmplitude) {
getOutputConnection(0)->reconfigure(0, newOutputSize);
setOutputNode(amplitude_panner);
current_panner = amplitude_panner;
}
if(hookHrtf) {
getOutputConnection(0)->reconfigure(0, 2);
setOutputNode(hrtf_panner);
current_panner = hrtf_panner;
}
}
EnvironmentNode::EnvironmentNode(std::shared_ptr<Simulation> simulation, std::shared_ptr<HrtfData> hrtf): SubgraphNode(Lav_OBJTYPE_ENVIRONMENT_NODE, simulation) {
this->hrtf = hrtf;
int channels = getProperty(Lav_ENVIRONMENT_OUTPUT_CHANNELS).getIntValue();
output = createGainNode(simulation);
output->resize(channels, channels);
output->appendInputConnection(0, channels);
output->appendOutputConnection(0, channels);
appendOutputConnection(0, channels);
setOutputNode(output);
environment_info.world_to_listener_transform = glm::lookAt(
glm::vec3(0.0f, 0.0f, 0.0f),
glm::vec3(0.0f, 0.0f, -1.0f),
glm::vec3(0.0f, 1.0f, 0.0f));
}
PannerBankNode::PannerBankNode(std::shared_ptr<Simulation> sim, int pannerCount, std::shared_ptr<HrtfData> hrtf): SubgraphNode(Lav_OBJTYPE_PANNER_BANK_NODE, sim) {
if(pannerCount < 2) ERROR(Lav_ERROR_RANGE, "Must use at least 2 panners.");
input_gain = createGainNode(simulation);
output_gain =createGainNode(simulation);
input_gain->resize(pannerCount, pannerCount);
for(int i = 0; i < pannerCount; i++) {
input_gain->appendInputConnection(i, 1);
input_gain->appendOutputConnection(i, 1);
}
output_gain->resize(2, 2);
output_gain->appendInputConnection(0, 2);
output_gain->appendOutputConnection(0, 2);
setInputNode(input_gain);
setOutputNode(output_gain);
for(int i = 0; i < pannerCount; i++) {
panners.push_back(createMultipannerNode(simulation, hrtf));
input_gain->connect(i, panners[i], 0);
panners[i]->connect(0, output_gain, 0);
}
appendOutputConnection(0, 2);
}
void NeuralNetwork::calculate()
{
// 中間層の計算
for(int m = 0; m < middle_nodes.size(); ++m)
{
Node middle = middle_nodes.at(m);
middle.value = 0;
vector<Edge> edges = getBeforeEdges(middle.id);
for(int i = 0; i < input_nodes.size(); ++i)
{
Edge e = edges.at(i);
middle.value += e.weight * input_nodes.at(i).value;
}
middle.value = sigmoid(middle.value);
setMiddleNode(middle.id, middle.value);
}
// 出力層の計算
for(int o = 0; o < output_nodes.size(); ++o)
{
Node output = output_nodes.at(o);
output.value = 0;
vector<Edge> edges = getAfterEdges(output.id);
for(int m = 0; m < middle_nodes.size(); ++m)
{
Edge e = edges.at(m);
output.value += e.weight * middle_nodes.at(m).value;
}
output.value = sigmoid(output.value);
setOutputNode(output.id, output.value);
}
}
