Crate autoeq_cea2034 

AutoEQ CEA2034 Scoring

This crate implements CEA2034-based preference scoring algorithms for loudspeaker measurements, based on research by Harman, Olive, and others.

Features

• CEA2034 Metrics Computation: Calculate standard metrics from spinorama measurements
• Preference Score Calculation: Harman/Olive preference rating algorithm
• Curve Analysis: Slope, smoothness, and spectral analysis tools
• PIR Computation: Predicted In-Room response calculation from CEA2034 data
• Octave Band Processing: Frequency-weighted analysis and filtering

CEA2034 Standard

The CEA2034 standard defines a set of measurements for evaluating loudspeaker performance:

• On-axis (0°): Direct response
• Listening Window: Average of on-axis and early reflections (±10°, ±15°, ±20°, ±25°, ±30°)
• Early Reflections: Floor, ceiling, front/rear wall, and side wall reflections
• Sound Power: Total acoustic power output
• Directivity Index (DI): Ratio of on-axis to sound power

Two examples of CEA2034 / Spinorama

Preference Score Algorithm

The preference score is based on research showing correlation between measured responses and listener preference:

use autoeq_cea2034::{compute_cea2034_metrics, Curve};
use ndarray::Array1;
use std::collections::HashMap;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let frequencies = Array1::from(vec![20.0, 25.0, 31.5, /* ... */ 20000.0]);

    // Example spinorama data - would typically come from measurements
    let mut spinorama_data = HashMap::new();
    spinorama_data.insert("On Axis".to_string(), Curve {
        freq: frequencies.clone(),
        spl: Array1::from(vec![-2.1, -1.8, -1.2, /* ... */ -10.5]),
        phase: None,
    });
    spinorama_data.insert("Listening Window".to_string(), Curve {
        freq: frequencies.clone(),
        spl: Array1::from(vec![-2.0, -1.7, -1.1, /* ... */ -10.3]),
        phase: None,
    });
    spinorama_data.insert("Sound Power".to_string(), Curve {
        freq: frequencies.clone(),
        spl: Array1::from(vec![-2.5, -2.0, -1.5, /* ... */ -11.0]),
        phase: None,
    });
    spinorama_data.insert("Estimated In-Room Response".to_string(), Curve {
        freq: frequencies.clone(),
        spl: Array1::from(vec![-2.2, -1.9, -1.3, /* ... */ -10.7]),
        phase: None,
    });

    let equalized_response = Array1::from(vec![0.5, 0.3, 0.1, /* ... */ -1.0]);

    let metrics = compute_cea2034_metrics(
        &frequencies,
        &spinorama_data,
        Some(&equalized_response)
    ).await?;

    println!("Preference Score: {:.2}", metrics.pref_score);
    Ok(())
}

Scoring Components

Listening Window (LW) Score

• Flatness and smoothness of the listening window response
• Deviation from target curve
• Frequency-weighted penalties

Predicted In-Room (PIR) Score

• Computed from listening window, early reflections, and sound power
• Represents typical in-room response
• Critical for overall preference rating

Bass Extension

• Low-frequency response evaluation
• Extension and smoothness below 100 Hz

Directivity Analysis

• Consistency of off-axis response
• Smoothness of directivity index
• Early reflection characteristics

Usage

use autoeq_cea2034::{score, octave_intervals, compute_pir_from_lw_er_sp};
use ndarray::Array1;

// Example frequency and response data
let frequencies = Array1::from(vec![20.0, 25.0, 31.5, /* ... */ 20000.0]);
let on_axis = Array1::from(vec![-2.1, -1.8, -1.2, /* ... */ -10.5]);
let listening_window = Array1::from(vec![-2.0, -1.7, -1.1, /* ... */ -10.3]);
let sound_power = Array1::from(vec![-2.5, -2.0, -1.5, /* ... */ -11.0]);
let pir_response = Array1::from(vec![-2.2, -1.9, -1.3, /* ... */ -10.7]);

// Compute octave band intervals for analysis
let intervals = octave_intervals(2, &frequencies);

// Compute preference score for the frequency response
let preference_metrics = score(
    &frequencies,
    &intervals,
    &on_axis,
    &listening_window,
    &sound_power,
    &pir_response
);

println!("Preference Score: {:.2}", preference_metrics.pref_score);

// Compute PIR from CEA2034 measurements
let lw_curve = Array1::from(vec![-2.0, -1.7, -1.1, /* ... */ -10.3]);
let er_curve = Array1::from(vec![-2.3, -2.1, -1.6, /* ... */ -10.8]);
let sp_curve = Array1::from(vec![-2.5, -2.0, -1.5, /* ... */ -11.0]);
let computed_pir = compute_pir_from_lw_er_sp(&lw_curve, &er_curve, &sp_curve);

Integration

This crate is part of the AutoEQ ecosystem:

• Used by autoeq for optimization target scoring
• Provides objective functions for autoeq-de optimization
• Integrates with measurement data from Spinorama.org API

Research Background

Based on published research:

• Olive, S. E., & Toole, F. E. (1989). “The detection of reflections in typical rooms”
• Olive, S. E. (2004). “A method for training listeners and selecting program material”
• Harman International patent applications on preference scoring algorithms

License

GPL-3.0-or-later

Structs

Curve

A struct to hold frequency and SPL data. Re-exported from the main autoeq crate for compatibility

DirectivityCurve

A single directivity measurement at a specific angle

DirectivityData

Complete directivity data for horizontal and vertical planes

ScoreMetrics

Metrics computed for the CEA2034 preference score

Functions

cea2034

Compute the CEA2034 spinorama from SPL data

compute_cea2034_metrics

Compute CEA2034 metrics for speaker performance evaluation

compute_pir_from_lw_er_sp

Compute Predicted In-Room (PIR) response from LW, ER, and SP measurements

lfx

Compute the Low Frequency Extension (LFX) metric

nbd

Compute the Narrow Band Deviation (NBD) metric

octave

Generate octave band frequencies

octave_intervals

Compute octave band intervals for a given frequency array

score

Compute all CEA2034 metrics and preference score

score_peq

Compute CEA2034 metrics and preference score for a PEQ filter

score_peq_approx

Compute approximate CEA2034 metrics and preference score for a PEQ filter

sm

Compute the Smoothness Metric (SM)