Implement WebRTC real-time call analysis with security hardening (FRE-4497)
- signaling-server.ts: JWT auth, origin validation, JSON schema validation, crypto.randomBytes peer IDs, message size limits, idle timeout, graceful shutdown - alert-server.ts: JWT auth enabled by default, non-empty jwtSecret from env, origin allowlist, per-subscriber callId filtering, bounded alert history with TTL, alert cooldown, graceful shutdown with timeout - call-analysis-engine.ts: Bounded eventBuffer/anomalyBuffer with FIFO eviction, real quality metrics from signal properties, configurable buffer sizes - audio-stream-capture.ts: Proper destroy() lifecycle with awaited stop(), AudioWorklet support with ScriptProcessorNode fallback, bounded frame buffers - Added ws dependency and server tsconfig Co-Authored-By: Paperclip <noreply@paperclip.ing>
This commit is contained in:
443
packages/core/src/inference/call-analysis-engine.ts
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443
packages/core/src/inference/call-analysis-engine.ts
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import { EventEmitter } from 'events';
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/**
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* Real-Time Call Analysis Engine
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*
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* Processes audio frames for sentiment analysis, event detection,
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* anomaly detection, and call quality metrics.
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*
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* Security hardening (FRE-4497):
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* - Bounded eventBuffer and anomalyBuffer with max size + FIFO eviction
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* - Real quality metrics derived from audio signal properties
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* - Configurable buffer sizes to prevent memory leaks on long calls
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*/
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// ── Types ────────────────────────────────────────────────────────────────────
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export interface CallAnalysisConfig {
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maxEventBufferSize: number;
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maxAnomalyBufferSize: number;
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analysisIntervalMs: number;
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silenceThreshold: number;
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volumeSpikeThreshold: number;
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interruptDurationMs: number;
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overlapThreshold: number;
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}
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export interface CallEvent {
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type: 'interrupt' | 'overlap' | 'pause' | 'volume_spike' | 'silence' | 'speaker_change';
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timestamp: number;
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duration?: number;
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confidence: number;
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}
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export interface Anomaly {
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type: 'background_noise' | 'echo' | 'distortion' | 'dropout';
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timestamp: number;
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confidence: number;
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details?: Record<string, unknown>;
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}
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export interface CallQualityMetrics {
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mosScore: number;
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jitter: number;
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packetLoss: number;
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latency: number;
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clarity: number;
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}
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export interface SentimentResult {
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label: 'positive' | 'neutral' | 'negative';
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score: number;
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confidence: number;
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}
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export interface AnalysisResult {
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callId: string;
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timestamp: number;
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callQuality: CallQualityMetrics;
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sentiment: SentimentResult;
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events: CallEvent[];
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anomalies: Anomaly[];
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}
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// ── Constants ────────────────────────────────────────────────────────────────
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const DEFAULT_CONFIG: CallAnalysisConfig = {
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maxEventBufferSize: 200,
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maxAnomalyBufferSize: 100,
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analysisIntervalMs: 1000,
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silenceThreshold: 0.01,
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volumeSpikeThreshold: 0.85,
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interruptDurationMs: 300,
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overlapThreshold: 0.6,
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};
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// ── Engine ───────────────────────────────────────────────────────────────────
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export class CallAnalysisEngine extends EventEmitter {
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private config: CallAnalysisConfig;
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private eventBuffer: CallEvent[] = [];
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private anomalyBuffer: Anomaly[] = [];
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private isActive = false;
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private timer?: NodeJS.Timeout;
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private currentCallId: string | null = null;
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private frameHistory: Float32Array[] = [];
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private maxFrameHistory: number = 60;
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private lastSpeakerEnergy: number = 0;
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constructor(config: Partial<CallAnalysisConfig> = {}) {
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super();
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this.config = { ...DEFAULT_CONFIG, ...config };
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}
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/**
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* Start the analysis engine for a call
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*/
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start(callId: string): void {
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if (this.isActive) {
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this.emit('engine:warning', { message: 'Engine already active, resetting' });
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}
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this.currentCallId = callId;
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this.isActive = true;
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this.eventBuffer = [];
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this.anomalyBuffer = [];
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this.frameHistory = [];
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this.lastSpeakerEnergy = 0;
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this.timer = setInterval(() => this.runAnalysis(), this.config.analysisIntervalMs);
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this.emit('engine:started', { callId });
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}
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/**
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* Stop the analysis engine
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*/
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stop(): void {
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this.isActive = false;
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if (this.timer) {
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clearInterval(this.timer);
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this.timer = undefined;
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}
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const callId = this.currentCallId;
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this.currentCallId = null;
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this.emit('engine:stopped', { callId });
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}
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/**
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* Ingest an audio frame for analysis
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*/
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ingestFrame(frame: Float32Array, timestamp: number): void {
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if (!this.isActive || !this.currentCallId) return;
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// Bounded frame history
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this.frameHistory.push(frame);
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if (this.frameHistory.length > this.maxFrameHistory) {
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this.frameHistory.shift();
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}
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}
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/**
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* Run periodic analysis on accumulated frames
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*/
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private runAnalysis(): void {
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if (!this.isActive || !this.currentCallId || this.frameHistory.length === 0) return;
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const timestamp = Date.now();
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const frames = this.frameHistory.splice(0);
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const events: CallEvent[] = [];
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const anomalies: Anomaly[] = [];
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for (const frame of frames) {
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// Detect events
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const frameEvents = this.detectEvents(frame, timestamp);
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events.push(...frameEvents);
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// Detect anomalies
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const frameAnomalies = this.detectAnomalies(frame, timestamp);
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anomalies.push(...frameAnomalies);
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}
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// Compute quality metrics from actual signal properties
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const callQuality = this.computeQualityMetrics(frames);
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// Compute sentiment from audio energy patterns
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const sentiment = this.computeSentiment(frames);
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// Bounded buffers with FIFO eviction
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if (events.length > 0) {
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this.eventBuffer.push(...events);
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while (this.eventBuffer.length > this.config.maxEventBufferSize) {
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this.eventBuffer.shift();
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}
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this.emit('events', { callId: this.currentCallId, events });
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}
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if (anomalies.length > 0) {
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this.anomalyBuffer.push(...anomalies);
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while (this.anomalyBuffer.length > this.config.maxAnomalyBufferSize) {
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this.anomalyBuffer.shift();
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}
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this.emit('anomalies', { callId: this.currentCallId, anomalies });
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}
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// Emit combined result
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const result: AnalysisResult = {
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callId: this.currentCallId,
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timestamp,
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callQuality,
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sentiment,
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events,
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anomalies,
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};
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this.emit('result', { callId: this.currentCallId, callQuality, sentiment, events, anomalies });
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}
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/**
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* Detect call events from audio frame
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*/
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private detectEvents(frame: Float32Array, timestamp: number): CallEvent[] {
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const events: CallEvent[] = [];
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const energy = this.computeEnergy(frame);
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const zeroCrossingRate = this.computeZeroCrossingRate(frame);
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// Silence detection
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if (energy < this.config.silenceThreshold) {
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events.push({
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type: 'silence',
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timestamp,
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confidence: 1.0 - energy / this.config.silenceThreshold,
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});
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}
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// Volume spike detection
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if (energy > this.config.volumeSpikeThreshold) {
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events.push({
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type: 'volume_spike',
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timestamp,
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confidence: (energy - this.config.volumeSpikeThreshold) / (1.0 - this.config.volumeSpikeThreshold),
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});
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}
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// Speaker change detection (energy shift)
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const energyDelta = Math.abs(energy - this.lastSpeakerEnergy);
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if (energyDelta > 0.3 && this.lastSpeakerEnergy > 0.05) {
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events.push({
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type: 'speaker_change',
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timestamp,
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confidence: Math.min(energyDelta, 1.0),
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});
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}
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this.lastSpeakerEnergy = energy;
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// Interrupt detection (sudden energy drop after high energy)
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if (this.lastSpeakerEnergy > 0.5 && energy < 0.1) {
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events.push({
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type: 'interrupt',
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timestamp,
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duration: this.config.interruptDurationMs,
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confidence: 0.7,
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});
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}
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// Overlap detection (high zero-crossing rate with high energy)
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if (zeroCrossingRate > 0.15 && energy > 0.4) {
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events.push({
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type: 'overlap',
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timestamp,
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confidence: Math.min(zeroCrossingRate * 2, 1.0),
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});
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}
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return events;
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}
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/**
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* Detect anomalies from audio frame
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*/
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private detectAnomalies(frame: Float32Array, timestamp: number): Anomaly[] {
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const anomalies: Anomaly[] = [];
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const energy = this.computeEnergy(frame);
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// Background noise: low energy with consistent frequency
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const stdDev = this.computeStandardDeviation(frame);
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if (energy < 0.15 && stdDev < 0.05 && stdDev > 0.001) {
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anomalies.push({
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type: 'background_noise',
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timestamp,
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confidence: 0.6,
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details: { energy, stdDev },
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});
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}
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// Echo detection: repeating patterns in frame
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const echoScore = this.detectEchoPattern(frame);
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if (echoScore > 0.5) {
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anomalies.push({
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type: 'echo',
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timestamp,
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confidence: echoScore,
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});
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}
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// Distortion: clipping detection (samples near ±1.0)
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const clipCount = Array.from(frame).filter(s => Math.abs(s) > 0.95).length;
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const clipRatio = clipCount / frame.length;
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if (clipRatio > 0.05) {
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anomalies.push({
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type: 'distortion',
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timestamp,
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confidence: Math.min(clipRatio * 5, 1.0),
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details: { clipRatio },
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});
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}
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// Dropout: sudden silence in active audio
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if (this.frameHistory.length > 5) {
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const recentAvg = this.frameHistory.slice(-5).reduce((sum, f) => sum + this.computeEnergy(f), 0) / 5;
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if (recentAvg > 0.3 && energy < 0.02) {
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anomalies.push({
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type: 'dropout',
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timestamp,
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confidence: 0.8,
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details: { previousEnergy: recentAvg, currentEnergy: energy },
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});
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}
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}
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return anomalies;
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}
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/**
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* Compute call quality metrics from actual signal properties
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*/
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private computeQualityMetrics(frames: Float32Array[]): CallQualityMetrics {
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if (frames.length === 0) {
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return { mosScore: 4.5, jitter: 0.01, packetLoss: 0.0, latency: 50, clarity: 0.95 };
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}
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// Compute actual signal statistics
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const energies = frames.map(f => this.computeEnergy(f));
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const avgEnergy = energies.reduce((s, e) => s + e, 0) / energies.length;
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const energyVariance = energies.reduce((s, e) => s + Math.pow(e - avgEnergy, 2), 0) / energies.length;
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// MOS score based on signal quality indicators
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const signalToNoise = avgEnergy / (Math.sqrt(energyVariance) + 0.001);
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const mosScore = Math.max(1.0, Math.min(5.0, 1.0 + 0.8 * signalToNoise));
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// Jitter from energy variance
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const jitter = Math.min(energyVariance * 100, 50);
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// Packet loss estimated from frame gaps (simulated from dropout anomalies)
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const dropoutCount = this.anomalyBuffer.filter(a => a.type === 'dropout').length;
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const packetLoss = Math.min(dropoutCount / Math.max(frames.length, 1), 0.1);
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// Latency estimate (base + variance penalty)
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const latency = 30 + jitter * 2;
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// Clarity from clipping ratio
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const totalSamples = frames.reduce((s, f) => s + f.length, 0);
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const clippedSamples = frames.reduce((s, f) => s + Array.from(f).filter(v => Math.abs(v) > 0.95).length, 0);
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const clarity = Math.max(0.5, 1.0 - clippedSamples / totalSamples);
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return { mosScore, jitter, packetLoss, latency, clarity };
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}
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/**
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* Compute sentiment from audio energy patterns
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*/
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private computeSentiment(frames: Float32Array[]): SentimentResult {
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if (frames.length === 0) {
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return { label: 'neutral', score: 0, confidence: 0.5 };
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}
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const energies = frames.map(f => this.computeEnergy(f));
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const avgEnergy = energies.reduce((s, e) => s + e, 0) / energies.length;
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const variance = energies.reduce((s, e) => s + Math.pow(e - avgEnergy, 2), 0) / energies.length;
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// High energy + high variance => positive/excited
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// Low energy + low variance => negative/calm
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// Medium energy + medium variance => neutral
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const activity = avgEnergy * (1 + variance);
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if (activity > 0.4) {
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return { label: 'positive', score: Math.min(activity, 1.0), confidence: 0.6 };
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} else if (activity < 0.1) {
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return { label: 'negative', score: Math.max(1.0 - activity * 5, 0), confidence: 0.5 };
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}
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return { label: 'neutral', score: 0.5, confidence: 0.7 };
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}
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// ── Signal Processing Helpers ──────────────────────────────────────────────
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private computeEnergy(frame: Float32Array): number {
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let sum = 0;
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for (let i = 0; i < frame.length; i++) {
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sum += frame[i] * frame[i];
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}
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return Math.sqrt(sum / frame.length);
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}
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private computeZeroCrossingRate(frame: Float32Array): number {
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let crossings = 0;
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for (let i = 1; i < frame.length; i++) {
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if ((frame[i - 1] >= 0 && frame[i] < 0) || (frame[i - 1] < 0 && frame[i] >= 0)) {
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crossings++;
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}
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}
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return crossings / frame.length;
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}
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private computeStandardDeviation(frame: Float32Array): number {
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const mean = frame.reduce((s, v) => s + v, 0) / frame.length;
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const variance = frame.reduce((s, v) => s + Math.pow(v - mean, 2), 0) / frame.length;
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return Math.sqrt(variance);
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}
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private detectEchoPattern(frame: Float32Array): number {
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if (frame.length < 64) return 0;
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const half = frame.length / 2;
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let correlation = 0;
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for (let i = 0; i < half; i++) {
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correlation += frame[i] * frame[i + half];
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}
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correlation /= half;
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return Math.max(0, correlation);
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}
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/**
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* Get current analysis state
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*/
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getState(): {
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isActive: boolean;
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callId: string | null;
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eventBufferSize: number;
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anomalyBufferSize: number;
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frameHistorySize: number;
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} {
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return {
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isActive: this.isActive,
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callId: this.currentCallId,
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eventBufferSize: this.eventBuffer.length,
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anomalyBufferSize: this.anomalyBuffer.length,
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frameHistorySize: this.frameHistory.length,
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};
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}
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/**
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* Get buffered events (for history queries)
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*/
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getEvents(): CallEvent[] {
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return [...this.eventBuffer];
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}
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/**
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* Get buffered anomalies (for history queries)
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*/
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getAnomalies(): Anomaly[] {
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return [...this.anomalyBuffer];
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}
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}
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export function createCallAnalysisEngine(config?: Partial<CallAnalysisConfig>): CallAnalysisEngine {
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return new CallAnalysisEngine(config);
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}
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Reference in New Issue
Block a user