投稿者: KIRISHIKI Yudai

    # func_ce12
    def _generate_lsp_from_params(self, params: LspParameters):
        """
        Generate LSP parameters from codebook indices.

        Args:
            params: LspParameters containing codebook indices and voiced flag
        """
        # Get appropriate weights based on voiced flag
        is_voiced = params.voiced_flag == 1
        base_weights, history_weights = self._get_weights(is_voiced, "generation")

        # Initialize LSP parameters
        lsp_params = np.zeros(self.ORDER, dtype=np.int16)

        # Get values from codebooks and convert to int16
        main_codebook = np.array(LSP_MAIN_CODEBOOK, dtype=np.uint16).astype(np.int16)
        sub_codebook = np.array(LSP_SUB_CODEBOOK, dtype=np.uint16).astype(np.int16)

        main_value = main_codebook[params.main_index]
        sub_value_1 = sub_codebook[params.sub_index_1]
        sub_value_2 = sub_codebook[params.sub_index_2]

        # Combine main and sub values
        lsp_params[:5] = main_value[:5] + sub_value_1[:5]
        lsp_params[5:] = main_value[5:] + sub_value_2[5:]

        # Apply spacing adjustments
        # ar0 = 10, ar2 = 0x0060
        self._enforce_minimum_lsp_separation(lsp_params, 10)
        # ar0 = 5, ar2 = 0x0060
        self._enforce_minimum_lsp_separation(lsp_params, 5)

        # Apply smoothing filter
        # ar1 = 0x7BEC, ar2 = 0x0060, ar3 = 0xF205, ar4 = 0x6C8A, ar5 = 0xF1C9
        self._current_lsp_params = self._apply_smoothing_filter(
            lsp_params, base_weights, self._lsp_history, history_weights
        )

        # Update history with new parameters
        # ar2 = 0x0060, ar3 = 0x6C8A
        self._update_history(lsp_params)

        # Stabilize and store results
        # ar2 = 0x7BEC
        self._enforce_lsp_stability_constraints(self._current_lsp_params)

from typing import Literal
import numpy as np
from numpy.typing import NDArray

from .frame import Frame, LspParameters
from .lsp_constants import (
    LSP_MAIN_CODEBOOK,
    LSP_SUB_CODEBOOK,
    LSP_UNVOICED_HISTORY_WEIGHTS,
    LSP_VOICED_HISTORY_WEIGHTS,
    LSP_UNVOICED_BASE_WEIGHTS,
    LSP_VOICED_BASE_WEIGHTS,
    LSP_UNVOICED_PREDICTION_WEIGHTS,
    LSP_VOICED_PREDICTION_WEIGHTS,
    LSP_COSINE_LOOKUP_TABLE,
    LSP_SINE_LOOKUP_TABLE,
)


class Mcelp:
    # LSP configuration constants
    ORDER = 10  # Number of LSP coefficients per frame

    # LSP stability constraint constants
    MIN_LSP_VALUE = 0x29  # Minimum value for first LSP
    MAX_LSP_VALUE = 0x6452  # Maximum value for last LSP
    MIN_LSP_SPACING = 0x141  # Minimum spacing between adjacent LSPs

    # LSP frequency domain transformation constants
    LSP_SCALING = 0x517D  # Initial scaling factor
    MAX_TABLE_INDEX = 0x3F  # Maximum lookup table index (63)

    # Default LSP values for initialization
    # fmt: off
    _DEFAULT_LSP_PARAMS = np.array(
        [0x0924, 0x1247, 0x1B6B, 0x248F, 0x2DB2, 0x36D6, 0x3FF9, 0x491D, 0x5241, 0x5B64],
        dtype=np.uint16
    ).astype(np.int16)

    _DEFAULT_FREQUENCY_DOMAIN_LSP_PARAMS = np.array(
        [0x7AD1, 0x6BB0, 0x53D4, 0x352C, 0x1237, 0xEDC9, 0xCAD4, 0xAC2C, 0x9450, 0x852F],
        dtype=np.uint16
    ).astype(np.int16)
    # fmt: on

    def __init__(self) -> None:
        """Initialize MCELP with default state."""
        self._reset_state()

    def _reset_state(self) -> None:
        """Reset internal state to default values."""
        # 0x6C8A
        self._lsp_history = np.stack([self._DEFAULT_LSP_PARAMS] * 3)
        # 0x7BEC
        self._current_lsp_params = self._DEFAULT_LSP_PARAMS.copy()
        # 0x6CB2
        self._latest_lsp_params = self._DEFAULT_LSP_PARAMS.copy()
        # 0x6F34
        self._current_frequency_domain_lsp_params = (
            self._DEFAULT_FREQUENCY_DOMAIN_LSP_PARAMS.copy()
        )
        # 0x6CBC
        self._latest_frequency_domain_lsp_params = (
            self._DEFAULT_FREQUENCY_DOMAIN_LSP_PARAMS.copy()
        )
        self._voiced_flag = False

    def _get_weights(
        self, voiced_flag: bool, weight_type: Literal["generation", "prediction"]
    ) -> tuple[np.ndarray, np.ndarray]:
        """
        Get appropriate weights based on voice flag and weight type.

        Args:
            voiced_flag: True if frame is voiced, False otherwise
            weight_type: Type of weights to retrieve ("generation" or "prediction")

        Returns:
            Tuple of (base_weights, history_weights)
        """
        # Select the appropriate base weights based on weight type and voiced flag
        if weight_type == "generation":
            base_weights_source = (
                LSP_VOICED_BASE_WEIGHTS if voiced_flag else LSP_UNVOICED_BASE_WEIGHTS
            )
        else:  # prediction
            base_weights_source = (
                LSP_VOICED_PREDICTION_WEIGHTS
                if voiced_flag
                else LSP_UNVOICED_PREDICTION_WEIGHTS
            )

        # Convert base weights to int16
        base_weights = np.array(base_weights_source, dtype=np.uint16).astype(np.int16)

        # Get history weights (same for both generation and prediction)
        history_weights_source = (
            LSP_VOICED_HISTORY_WEIGHTS if voiced_flag else LSP_UNVOICED_HISTORY_WEIGHTS
        )
        history_weights = np.array(history_weights_source, dtype=np.uint16).astype(
            np.int16
        )

        return base_weights, history_weights

    # func_ca35
    def _enforce_minimum_lsp_separation(
        self,
        lsp_params: NDArray[np.int16],  # ar2
        threshold: int,  # ar0
    ) -> None:
        """
        Enforce minimum separation between adjacent LSP parameters.

        Args:
            lsp_params: Array of LSP parameters to adjust
            threshold: Minimum separation threshold
        """
        for i in range(self.ORDER - 1):
            # Calculate difference between current and next parameter
            difference = lsp_params[i] - lsp_params[i + 1]
            adjusted_difference = difference + threshold

            # If parameters are too close, adjust them equally in opposite directions
            if adjusted_difference > 0:
                half_adjustment = adjusted_difference >> 1
                lsp_params[i] -= half_adjustment
                lsp_params[i + 1] += half_adjustment

    # func_c960
    def _apply_smoothing_filter(
        self,
        current_values: NDArray[np.int16],  # ar2
        current_weights: NDArray[np.int16],  # ar3
        history_values: NDArray[np.int16],  # ar4
        history_weights: NDArray[np.int16],  # ar5
    ) -> NDArray[np.int16]:  # ar1
        """
        Apply smoothing filter to LSP parameters using weighted history.

        Args:
            current_values: Current LSP parameters
            current_weights: Weights for current parameters
            history_values: Historical LSP parameters
            history_weights: Weights for historical parameters

        Returns:
            Filtered LSP parameters
        """
        # Convert to int64 to prevent overflow during calculations
        current_values = current_values.astype(np.int64)
        current_weights = current_weights.astype(np.int64)
        history_values = history_values.astype(np.int64)
        history_weights = history_weights.astype(np.int64)

        filtered_values = np.zeros(self.ORDER, dtype=np.uint16)

        for i in range(self.ORDER):
            accumulator = current_values[i] * current_weights[i]

            for j in range(3):  # Process 3 frames of history
                accumulator += history_values[j][i] * history_weights[j][i]

            # Scale down by 2^15
            filtered_values[i] = accumulator >> 15

        return filtered_values.astype(np.int16)

    # func_c941
    def _predict_lsp_with_feedback(
        self,
        current_values: NDArray[np.int16],  # ar1
        current_weights: NDArray[np.int16],  # ar4
        history_values: NDArray[np.int16],  # ar2
        history_weights: NDArray[np.int16],  # ar3
    ) -> NDArray[np.int16]:  # ar5
        """
        Predict LSP parameters with feedback mechanism.

        Args:
            current_values: Current LSP parameters
            current_weights: Weights for current parameters
            history_values: Historical LSP parameters
            history_weights: Weights for historical parameters

        Returns:
            Predicted LSP parameters
        """
        # Convert to int64 to prevent overflow during calculations
        current_values = current_values.astype(np.int64)
        current_weights = current_weights.astype(np.int64)
        history_values = history_values.astype(np.int64)
        history_weights = history_weights.astype(np.int64)

        predicted_values = np.zeros(self.ORDER, dtype=np.uint16)
        feedback = 0

        for i in range(self.ORDER):
            base = current_values[i] << 16

            for j in range(3):  # Process 3 frames of history
                base -= (history_values[j][i] * history_weights[j][i]) << 1

            intermediate = base & 0xFFFF

            base -= feedback

            feedback += (current_weights[i] * intermediate) << 1
            feedback >>= 16
            feedback += (current_weights[i] * (base >> 16)) << 1

            predicted_values[i] = feedback >> 13

        return predicted_values.astype(np.int16)

    # func_c9ce
    def _enforce_lsp_stability_constraints(
        self,
        lsp_params: NDArray[np.int16],  # ar2
    ) -> None:
        """
        Enforce stability constraints on LSP parameters:
        1. Ensure monotonic increase
        2. Apply minimum/maximum value constraints
        3. Ensure minimum spacing

        Args:
            lsp_params: LSP parameters to stabilize
        """
        # Step 1: Single pass to ensure monotonic increase
        for i in range(len(lsp_params) - 1):
            if lsp_params[i] > lsp_params[i + 1]:
                # Swap adjacent LSPs if out of order
                lsp_params[i], lsp_params[i + 1] = lsp_params[i + 1], lsp_params[i]

        # Step 2: Apply minimum value constraint to first LSP
        lsp_params[0] = max(lsp_params[0], self.MIN_LSP_VALUE)

        # Step 3: Ensure minimum spacing between adjacent LSPs
        # Uses original values as reference to prevent cascading shifts
        for i in range(len(lsp_params) - 1):
            min_next_value = lsp_params[i] + self.MIN_LSP_SPACING
            if lsp_params[i + 1] < min_next_value:
                lsp_params[i + 1] = min_next_value

        # Step 4: Apply maximum value constraint to last LSP
        lsp_params[-1] = min(lsp_params[-1], self.MAX_LSP_VALUE)

    # func_c7ee
    def _transform_lsp_to_frequency_domain(
        self, lsp_params: NDArray[np.int16]  # ar2
    ) -> NDArray[np.int16]:  # ar3
        """
        Transform LSP parameters from line-pair domain to frequency domain.

        Args:
            lsp_params: LSP parameters to transform

        Returns:
            Transformed frequency domain parameters
        """
        # Convert lookup tables to int64 to prevent overflow
        cos_table = (
            np.array(LSP_COSINE_LOOKUP_TABLE, dtype=np.uint16)
            .astype(np.int16)
            .astype(np.int64)
        )
        sin_table = (
            np.array(LSP_SINE_LOOKUP_TABLE, dtype=np.uint16)
            .astype(np.int16)
            .astype(np.int64)
        )

        scaled_values = np.zeros(self.ORDER, dtype=np.uint16)
        lsp_params = lsp_params.astype(np.uint64)

        for i in range(self.ORDER):
            # Initial scaling
            initial_product = (lsp_params[i] * self.LSP_SCALING) << 1

            # Calculate lookup table index and interpolation fraction
            table_index = initial_product >> 8
            table_index = min(table_index, self.MAX_TABLE_INDEX << 16) >> 16
            fraction = ((initial_product >> 16) & 0xFF) << 3

            # Linear interpolation between lookup table values
            base_value = cos_table[table_index] << 16
            scale_factor = sin_table[table_index]
            interpolation = (scale_factor * fraction).astype(np.int64) << 1

            scaled_values[i] = (base_value + interpolation).astype(np.int64) >> 16

        return scaled_values.astype(np.int16)

    # func_c9c2
    def _update_history(
        self,
        lsp_params: NDArray[np.int16],  # ar2
    ) -> None:
        """
        Update LSP parameter history by shifting and inserting new parameters.

        Args:
            lsp_params: New LSP parameters to add to history
        """
        # Shift history array and insert new parameters at index 0
        # self._lsp_history: ar3
        self._lsp_history = np.roll(self._lsp_history, 1, axis=0)
        self._lsp_history[0] = lsp_params.copy()

    # func_ce12
    def _generate_lsp_from_params(self, params: LspParameters):
        """
        Generate LSP parameters from codebook indices.

        Args:
            params: LspParameters containing codebook indices and voiced flag
        """
        # Get appropriate weights based on voiced flag
        is_voiced = params.voiced_flag == 1
        base_weights, history_weights = self._get_weights(is_voiced, "generation")

        # Initialize LSP parameters
        lsp_params = np.zeros(self.ORDER, dtype=np.int16)

        # Get values from codebooks and convert to int16
        main_codebook = np.array(LSP_MAIN_CODEBOOK, dtype=np.uint16).astype(np.int16)
        sub_codebook = np.array(LSP_SUB_CODEBOOK, dtype=np.uint16).astype(np.int16)

        main_value = main_codebook[params.main_index]
        sub_value_1 = sub_codebook[params.sub_index_1]
        sub_value_2 = sub_codebook[params.sub_index_2]

        # Combine main and sub values
        lsp_params[:5] = main_value[:5] + sub_value_1[:5]
        lsp_params[5:] = main_value[5:] + sub_value_2[5:]

        # Apply spacing adjustments
        # ar0 = 10, ar2 = 0x0060
        self._enforce_minimum_lsp_separation(lsp_params, 10)
        # ar0 = 5, ar2 = 0x0060
        self._enforce_minimum_lsp_separation(lsp_params, 5)

        # Apply smoothing filter
        # ar1 = 0x7BEC, ar2 = 0x0060, ar3 = 0xF205, ar4 = 0x6C8A, ar5 = 0xF1C9
        self._current_lsp_params = self._apply_smoothing_filter(
            lsp_params, base_weights, self._lsp_history, history_weights
        )

        # Update history with new parameters
        # ar2 = 0x0060, ar3 = 0x6C8A
        self._update_history(lsp_params)

        # Stabilize and store results
        # ar2 = 0x7BEC
        self._enforce_lsp_stability_constraints(self._current_lsp_params)

    # func_d071 Part 1
    def _generate_lsp_from_codebook(self, params: LspParameters) -> None:
        """
        Generate new LSP parameters from codebook indices.

        Args:
            params: LspParameters containing codebook indices and voiced flag
        """
        # ar1 = 0x7BEC, ar7 = 0x6C8A
        self._generate_lsp_from_params(params)
        # ar3(0x7BEC) -> ar2(0x6CB2)
        self._latest_lsp_params = self._current_lsp_params.copy()
        self._voiced_flag = params.voiced_flag == 1

    # func_d071 Part 2
    def _predict_lsp_parameters(self) -> None:
        """
        Predict LSP parameters based on history and current state.
        """
        # Get appropriate weights based on voiced flag
        base_weights, history_weights = self._get_weights(
            self._voiced_flag, "prediction"
        )

        # Copy latest parameters to current
        # ar2(0x6CB2) -> ar3(0x7BEC)
        self._current_lsp_params = self._latest_lsp_params.copy()

        # Calculate predicted parameters
        # ar1 = 0x6CB2, ar2 = 0x6C8A, ar3 = 0xF1E7, ar4 = 0xF223, ar5 = 0x7BE2
        predicted_lsp_params = self._predict_lsp_with_feedback(
            self._latest_lsp_params,
            base_weights,
            self._lsp_history,
            history_weights,
        )

        # Update history with predicted parameters
        # ar2 = 0x7BE2, ar3 = 0x6C8A
        self._update_history(predicted_lsp_params)

    # func_d071
    def process_frame(self, frame: Frame) -> np.ndarray:
        """
        Process a frame of speech data to generate or predict LSP parameters.

        Args:
            frame: Frame containing control bit and LSP parameters

        Returns:
            Frequency domain LSP parameters
        """
        if frame.control_bit == 0:
            # Generate new LSP from codebook
            self._generate_lsp_from_codebook(frame.lsp_params)
        else:
            # Predict LSP based on history
            self._predict_lsp_parameters()

        # Transform to frequency domain
        # ar2 = 0x7BEC, ar3 = 0x6F34
        self._current_frequency_domain_lsp_params = (
            self._transform_lsp_to_frequency_domain(self._latest_lsp_params)
        )

        return self._current_frequency_domain_lsp_params