bitte schreibe nachfolgenden Python Code in Mojo um. hier der Python code:    # **Teil 1**

import numpy as np
import librosa
from scipy import signal
import soundfile as sf
from numba import jit, cuda
import pyloudnorm as pyln
from pesq import pesq
from pystoi import stoi
import gpuRIR
import pyroomacoustics as pra
from sklearn.decomposition import FastICA
import torch
import torchaudio
from torch import nn
import torch.nn.functional as F
import pywt
import scipy.io as sio
import os

class AdvancedPsychoacousticModel(nn.Module):
    def init(self, n_fft, sr):
        super().init()
        self.n_fft = n_fft
        self.sr = sr
        self.bark_scale = self._compute_bark_scale()
        self.spreading_function = self._compute_spreading_function()
        self.threshold_in_quiet = self._compute_threshold_in_quiet()
        self.loudness_model = self._init_loudness_model()
        self.masking_model = self._init_masking_model()

    def _compute_bark_scale(self):
        freq = librosa.fft_frequencies(sr=self.sr, n_fft=self.n_fft)
        return 13 * np.arctan(0.00076 * freq) + 3.5 * np.arctan((freq / 7500) ** 2)

    def _compute_spreading_function(self):
        bark_diff = self.bark_scale[:, np.newaxis] - self.bark_scale[np.newaxis, :]
        spreading = 15.81 + 7.5 * (bark_diff + 0.474) - 17.5 * np.sqrt(1 + (bark_diff + 0.474) ** 2)
        return 10 ** (spreading / 10)

    def _compute_threshold_in_quiet(self):
        freq = librosa.fft_frequencies(sr=self.sr, n_fft=self.n_fft)
        threshold = 3.64 * (freq / 1000) ** -0.8 - 6.5 * np.exp(-0.6 * (freq / 1000 - 3.3) ** 2) + 1e-3 * (freq / 1000) ** 4
        return 10 ** (threshold / 10)

    def _init_loudness_model(self):
        return nn.Sequential(
            nn.Conv1d(1, 64, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv1d(64, 32, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv1d(32, 1, kernel_size=3, padding=1),
            nn.ReLU()
        )

    def _init_masking_model(self):
        return nn.Sequential(
            nn.Conv1d(1, 64, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv1d(64, 32, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv1d(32, 1, kernel_size=3, padding=1),
            nn.Sigmoid()
        )

    def forward(self, power_spectrum):
        ptm = self._peaksToMultiple(power_spectrum)
        spread_energy = torch.matmul(power_spectrum, torch.from_numpy(self.spreading_function.T).float().to(power_spectrum.device))
        masking_threshold = torch.max(spread_energy, torch.from_numpy(self.threshold_in_quiet).float().to(power_spectrum.device))
        masking_threshold = masking_threshold * 10 ** ((-1.525 - 0.275 * torch.from_numpy(self.bark_scale).float().to(power_spectrum.device) - 4.5 + ptm) / 10)
        
        loudness = self.loudness_model(power_spectrum.unsqueeze(1)).squeeze(1)
        masking = self.masking_model(power_spectrum.unsqueeze(1)).squeeze(1)
        
        return masking_threshold, loudness, masking

    def _peaksToMultiple(self, power_spectrum):
        peaks = signal.find_peaks(power_spectrum.cpu().numpy())[0]
        ptm = torch.zeros_like(power_spectrum)
        ptm[peaks] = 1
        return ptm

class AdaptiveEqualizer(nn.Module):
    def init(self, n_bands, lr=0.01):
        super().init()
        self.weights = nn.Parameter(torch.ones(n_bands))
        self.lr = lr

    def forward(self, x):
        equalized = x * self.weights.unsqueeze(1)
        target = torch.mean(x, dim=0, keepdim=True)
        error = target - equalized
        self.weights.data += self.lr * torch.mean(error * x, dim=1)
        return equalized

class EnhancedAudioVirtualizer(nn.Module):
    def init(self, n_channels, widening_factor=1.5):
        super().init()
        self.n_channels = n_channels
        self.widening_factor = widening_factor
        self.conv = nn.Conv1d(n_channels, n_channels, kernel_size=1024, padding=512)

    def forward(self, x):
        batch_size, n_channels, length = x.shape
        x_wide = self.conv(x)
        x_wide = x_wide * self.widening_factor

        # Apply sophisticated panning/rendering algorithms
        enhanced_rendered = self._apply_vbap(x + x_wide)
        
        return enhanced_rendered

    def _apply_vbap(self, audio):
        # Dummy implementation for VBAP
        return audio * self.widening_factor

class FlexibleRenderer(nn.Module):
    def init(self, input_channels, output_channels):
        super().init()
        self.input_channels = input_channels
        self.output_channels = output_channels
        self.channel_mapping = nn.Parameter(torch.randn(output_channels, input_channels))

    def forward(self, audio):
        return torch.matmul(self.channel_mapping, audio)

class EnhancedFlexibleRenderer(nn.Module):
    def init(self, input_channels, output_channels):
        super().init()
        self.input_channels = input_channels
        self.output_channels = output_channels
        self.channel_mapping = nn.Parameter(torch.randn(output_channels, input_channels))
        self.dynamic_range_compressor = nn.Parameter(torch.ones(output_channels))

    def forward(self, audio):
        mapped_audio = torch.matmul(self.channel_mapping, audio)
        compressed_audio = torch.tanh(mapped_audio * self.dynamic_range_compressor.unsqueeze(1))
        return compressed_audio

class AudioObjectDetector(nn.Module):
    def init(self):
        super().init()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(128 * 8 * 8, 512)
        self.fc2 = nn.Linear(512, 10)  # 10 Objektklassen
        self.localization = nn_linear(512, 3)  # x, y, z Koordinaten

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = self.pool(F.relu(self.conv3(x)))
        x = x.view(-1, 128 * 8 * 8)
        features = F.relu(self.fc1(x))
        classification = self.fc2(features)
        location = self.localization(features)
        return classification, location

class ObjectTracker:
    def __init__(self):
        self.objects = {}

    def update(self, detections):
        for obj_id, (obj_class, location) in enumerate(detections):
            if obj_id in self.objects:
                self.objects[obj_id]['trajectory'].append(location)
            else:
                self.objects[obj_id] = {
                    'class': obj_class,
                    'trajectory': [location]
                }
        
        # Simple filtering to smooth trajectories
        for obj_id in self.objects:
            if len(self.objects[obj_id]['trajectory']) > 5:
                self.objects[obj_id]['trajectory'] = self.objects[obj_id]['trajectory'][-5:]