University on Matias Di Bernardo

Acoustic modal response optimization for small rooms with genetic algorithms

Thu, 26 Jun 2025 00:00:00 +0000

This work was developed in the context of the course Instruments and Acoustic Measurements at UNTREF Argentina.

Introduction

Control rooms and critical listening environments often suffer from uneven low-frequency acoustic responses. These irregularities, caused by a clustered distribution of vibrational modes, produce “colorations” that hamper accurate sound evaluation. Traditionally, criteria such as those by Bonello, Bolt and Louden have been used to optimize the geometry of rectangular rooms and improve modal distribution. However, these methodologies do not consider the influence of complex boundaries nor the positions of source and listener.

This work presents an open-source tool developed in Python/FEniCS that addresses these limitations. The software uses geometric optimization by brute force over finite element models (FEM) to find room dimensions and contours that provide a more uniform modal distribution.

Theoretical Framework and Classical Criteria

Low-frequency behavior in an enclosure is dominated by standing waves, or normal modes, which are characterized by pressure nodes and antinodes. Axial, tangential and oblique modes — whose frequencies depend on the room dimensions — can produce coloration problems when they cluster.

Classical design criteria, such as those of Bolt, Bonello and Louden, focus on avoiding modal clustering and propose optimal geometric ratios for rectangular rooms. However, these approaches have a major limitation: they do not consider crucial factors such as the position of the sound source and the receiver, and they are restricted to simple geometries.

Methodology and Software Development

The developed tool combines a two-stage optimization process.

Initial Search: First, the software performs a rapid search on rectangular parallelepipeds using the classical modal superposition (MS) method to identify the most promising initial geometric proportions.
Refinement and Optimization: Then, it refines the search by generating random planar contours with enforced symmetry and applies the Frequency-Domain Finite Element Method (FD-FEM) to evaluate the acoustic merit of complex geometries. This method is more accurate than modal superposition for non-rectangular geometries.

To quantify acoustic performance, a combined figure of merit is used: the Mean Sound Field Deviation (MSFD). This metric integrates two key parameters:

Magnitude Deviation (MD): Measures how flat the frequency response is at a specific position.
Spatial Deviation (SD): Measures the variation of magnitude across the listening area.

The tool includes a graphical user interface (GUI) in PyQt5 that allows the user to define dimensions, margins, and source/receiver positions, and to visualize results and optimized geometries.

Results and Conclusions

Case studies on three reference control-room volumes showed MSFD improvements of up to 5 dB compared to the baseline design. The results demonstrate that:

Impact of Margins: As the available design space for optimization increases, better results are obtained, improving the overall response by up to 3 dB. This improvement is observed mainly in the Magnitude Deviation (MD).

Complex Geometries: Increasing the number of walls in a complex geometry produces solutions superior to simple rectangular parallelepipeds, with a mean difference of 1.3 dB in the merit factor. The optimization process does not yield a single solution but a variety of geometries that present a minimum MSFD.

Comparison with Traditional Criteria: A complex optimized geometry outperformed rooms dimensioned according to classic criteria by Bolt, Louden and Cox. Although these criteria are effective and computationally free, the software’s ability to model complex geometries and consider the locations of sources and receivers provides a superior condition.

The study concludes that the software is an effective tool for modal acoustic optimization. Future improvements are suggested, such as implementing more advanced optimization algorithms — for example, genetic algorithms — to reduce computation time and increase process efficiency.

A detailed analysis of the development of this algorithm is available in the following paper.

Acoustic measurement at Usina del Arte

Fri, 09 May 2025 00:00:00 +0000

Introduction

This measurement was part of the course Instruments and Acoustic Measurements of the Sound Engineering program at UNTREF. The acoustic parameters obtained from the impulse response are essential to evaluate the behavior of an enclosure. This report presents a comprehensive characterization of the main auditorium of the Usina del Arte, a cultural center in Buenos Aires. The building, originally a 20th-century power plant with a distinctive Florentine-industrial style, was transformed with an acoustic design that sought a natural and balanced quality without the need for amplification. A decoupled structure (box-in-box) was implemented for isolation and interior treatment with materials such as guatambú wood, diffusive surfaces, and a suspended acoustic reflector. The objective was a reverberation time of approximately 2 seconds and an even distribution of early lateral reflections for an enveloping sensation.

Measurement

The characterization was carried out on June 9, 2025, during which a total of 162 impulse responses (monaural and binaural) were recorded. Data were captured from 27 microphone positions and 3 source positions. An on-site survey of the auditorium was also performed to analyze its constructional characteristics and a perceptual analysis was conducted.

Prior to the measurements, a room model was created in EASE 4.3, which estimated a volume of 15,700 m³ and a Schroeder frequency of 22.1 Hz. Background noise was measured at eight positions to evaluate the isolation, confirming a signal-to-noise ratio greater than 40 dB. The microphone arrangement was based on the room’s symmetry to obtain a detailed mapping.

More images from the measurement process:

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Processing

Recordings were processed to obtain the impulse responses and various parameters were calculated following the ISO 3382-1 standard:

Reverberation time: $T_{20}$, $T_{30}$ and EDT.
Clarity: $C_{50}$ and $C_{80}$.
Strength (G): Difference in sound pressure level between the hall and an anechoic reference condition.
Lateral Fraction (LF): Proportion of sound energy perceived from the laterals.
Direct/reverberant ratio (D/R).
Intelligibility: The Speech Transmission Index (STI) and the Articulation Loss of Consonants (%Alcons) were calculated.
Stage support: $ST_{Early}$ and $ST_{Late}$, to assess acoustic conditions for musicians.

Various commercial software tools were used, such as the Aurora Acoustical Parameters plugin and the EASERA software, and additional parameters were computed with specific Python scripts.

Results

The results show that the auditorium behaves adequately for a concert hall, but with areas for improvement:

Reverberation time: The global average was 1.92 s. However, notable variations were observed at low frequencies, where the floating stage acts as a resonator.

Clarity and Intelligibility: Clarity values for speech are below recommended thresholds, and intelligibility issues were identified in certain zones.
Sound Strength (G): The sound strength level shows a low variation considering the auditorium’s dimensions.

Lateral Fraction (LF): Values exceed recommendations, suggesting that most of the sound energy comes from the laterals. This may be related to the large number of diffusers.
Background Noise: The room presents a noise level higher than recommended for a symphonic venue (NC-35 vs. NC-20), likely due to the ventilation system.

Sound Diffusion: Repetition of a single sequence of diffusers reduces their effectiveness, producing a lobed behavior instead of stochastic diffusion.

Key improvements are proposed, such as reducing background noise, optimizing sound diffusion with non-periodic sequences, and balancing the spectral response by correcting low-frequency absorption.

Conclusions

We were able to effectively characterize the auditorium and apply most of the theoretical topics covered in class to a practical experience. The full report of this work with all results and measurement details can be found in the following report.

THD+N Meter

Tue, 26 Nov 2024 00:00:00 +0000

THD+N Meter

A THD+N meter measures the harmonic distortion of a device. In audio, this is crucial for assessing the quality of equipment. This project was developed as part of the subject “Instrumentos y Mediciones Electrónicas” at UNTREF. In this course, students design and develop various electronic instruments, and projects are carried forward by successive student groups. This particular project was already underway, and our focus was on developing a phase shifter to align two signals.

Teory of THD measurement

To measure the distortion of a system, a sinusoidal signal with minimal distortion is input to the device under test. The goal is to evaluate how much distortion the device adds to the signal. This is quantified by measuring the power of the original signal and the power of the signal after passing through the device, excluding the fundamental harmonic.

A differential amplifier is used to subtract the device’s output signal from the reference signal, leaving only the higher-order harmonics.

The THD+N is then calculated as a percentage using the following equation:

$$ THD+N = \frac{V_{filt}}{V_{tot}} \cdot 100 $$

Where: $V_{filt}$ is the RMS value of the filtered signal (excluding the fundamental harmonic). $V_{tot}$ is the RMS value of the original signal.

Phase Shifter

To achieve effective cancellation in the differential section, precise phase and gain adjustments of the two signals are required. The gain adjustment had been successfully implemented by the previous group working on the project. However, achieving the necessary 360-degree phase rotation across the entire audible frequency range (20 Hz to 20 kHz) presented a challenge.

To address this, we designed two all-pass filters in series and calculated the component values to achieve the desired phase rotation over the target frequency range.

The design was modular to facilitate seamless integration with the previous stages.

We tested the circuit on a protoboard before designing the PCB using Altium Designer.

The device

The device features several controls for adjusting phase and gain. Both the phase and gain adjustments include fine-tuning potentiometers to ensure maximum precision.

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It is equipped with BNC inputs and outputs, allowing users to visualize the output on an oscilloscope and achieve maximum attenuation.

Results

This device was compared against a commercial THD meter (GW INSTEK GAD-201G), and the results were highly similar. The primary limitation was the base noise level of the measurement environment, which significantly restricted the lowest THD value we could measure.

The specifications of the device are summarized in the following table (in Spanish):

A detailed analysis of the device is available in this final report (written in spanish).

Building and design of a personal loudspeaker

Wed, 20 Nov 2024 00:00:00 +0000

“BassAdo”: A Semi-Portable Low-Cost Home Speaker

This project is part of the Electroacoustics II course at UNTREF within the Sound Engineering program. The task was to design a speaker system from scratch by applying the theory and concepts explained in class. The project was developed over the entire semester, with various stages to complete and present in reports. The speaker is intended for use in large spaces, potentially outdoors, to play music in a social gathering setting. It was named BassAdo to blend the Argentine tradition of “asado” (a typical barbecue gathering) with the word “bass,” emphasizing the speaker’s low-frequency performance.

Design

The goal was to design an accessible home audio system that allowed exploration of topics discussed in the course. The design aimed to emphasize bass response, characteristic of commercial systems, prioritizing low-frequency bandwidth extension over minimal group delay and system time control.

Regarding the transducers, the team had access to Yharo-brand units, which are classified as non-professional, consumer-grade, and suitable for automotive or home systems. The impedance response of the units was evaluated, and an 8” woofer was selected for low frequencies, along with two 4” units for mid/high frequencies.

Measuring the Thiele-Small parameters of the speakers revealed a high Vas (Equivalent Suspension Acoustic Volume), necessitating a large cabinet volume for proper control. To address this, and given the availability of two 8” woofers, the team opted for an isobaric speaker configuration, acoustically coupling the woofers to improve control and reduce cabinet size. Additionally, the cabinet was designed as vented to enhance low-frequency response.

The Thiele-Small parameters were obtained using the software REW. With these parameters, simulations were performed in Basta! to optimize the design for the desired response. A key focus was tuning the port’s resonance frequency to achieve strong low-frequency performance. The transducer had an fs of 45 Hz, and the port was tuned to 40 Hz by adjusting the tube length and cabinet air volume.

Based on these results, a 3D model of the cabinet was created using SolidWorks, and the design was used to cut the materials for construction.

Details of this process are documented in the following design report.

Construction

The wood was cut according to the 3D model, and the cabinet was assembled.

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As shown in the images, rock wool was added as an acoustic absorber. Measurements revealed this was excessive (the port resonance was overly damped), so some rock wool was removed to achieve the desired result.

Measurement and Calibration

Frequency response and directivity measurements were conducted in the university’s laboratory using the following equipment:

Powersoft M50Q amplifier
Earthworks M50 microphone
RME Fireface UCX audio interface
OUTLINE ET250-3D turntable

Using this setup and the Arta software, the acoustic response of individual transducers was characterized (useful for crossover filter simulation). Vertical and horizontal directivity responses were also evaluated to determine the best orientation for use. Frequency response graphs for both transducers were generated.

All measurements and in-depth analysis are included in the following measurement report.

Crossover Filter Design

Finally, the crossover filter stage was designed. Using the previous measurements, data were uploaded to VituixCad to calculate the simulations. The goal of the crossover filter was to achieve a pleasant frequency response for music playback and to enhance low frequencies. Vertical polar response uniformity was also a priority.

Since the power stage required an active supply, an active crossover filter with a Sallen-Key topology was implemented. The number of filters was defined based on space and cost, and adjustments were made in the software to achieve the desired response. For example, the low-frequency driver used the following configuration:

Where:

F1: High-pass fs=30 Hz | Q=0.67
F2: Low-pass fs=480 Hz | Q=0.5
F3: Notch filter at 220 Hz
F4: Notch filter at 400 Hz

Before building the filter, the proposed configuration was tested with a digital filter to practically evaluate the system’s response. Details of this section are provided in the following crossover filter report.

Conclusions

This project allowed us to apply theoretical concepts in practice and gain a deeper understanding of the development and challenges involved in designing an electroacoustic system.

Automatic Workout Routine Generator

Wed, 17 Apr 2024 00:00:00 +0000

Automatic Workout Routine Generator

This project was the final project asignment for the Algoritmos y Programación II on UNTREF. The idea of the application is to function as a smart workout planification, where the user input certain criterias and the program establish the best workout routine to maximice the users preference. The program was written in the Go programming language.

Data Management

This course does not focues on databases, so we choose to use a CSV to simulate a database. This file contains all the information of the different routines and the differente attributes. This serve as a presistency file wheras all the logic is handle by the program.

Different exercises are categorized by tematics. There are two entities on the program that are represented as structs in Go, this are the Exercise and the Routines.

Exercise

All the exercises have the following attributes:

Name: Name of the exercise.
Description: Detailed description of the exercise.
Duration: Estimated duration of the exercise.
Calories: Number of calories burned during the exercise.
Type: Type of exercise (e.g., cardio, strength, flexibility).
Muscle Group: Muscle group targeted by the exercise.
Points: Points assigned to the exercise for each of its types.
Difficulty: Difficulty level of the exercise.

Routines

All the routines are a collection of exercises. This are paresed as a linked list of exercise. Aside from this, the routins have the following attributes:

Name: Name of the routine.
Exercises: String that stores the IDs of the exercises in the routine, separated by commas.
AvailableExercises: Linked list of the exercises available for creating the routine.

Algorithm

The idea is to maximice different parameters, for example, maximum calories burnt in the least ammount of time, or the minimum duration for this muscle group or type of exercise. To find the best solutions based on the existing data we propose a dynamic programming algorithm that searches for all the posiblities from the data and find the maximum or minimum according to the specifications of the user. The DP apprach uses memory to avoid recalculation combinantions that already were computed, with this optimization the algorithm is fast enough that can generate the routines in miliseconds (with the test dataset under evaluation).

User Interface

At the moment, the programm is designed to be used form the terminal as a CLI application. The user can crear an exersice and a rutine, list the avaliable optionas, and generate a custom routine base on the specifications that he choose.

This is the first iteration of the project and it is fully functional but we plan to create a custom GUI to be user friendly, but for this proposse it will be better to use a different framework and use this GO application as the backend for a web or app service.

Conclusions

With this project I solidify my knowledege on programming topics like data structurs and algorithms because we used the theory seen in class and applied it to a real case scenario. Also it help me to get used to the Go langeage and it became a leangauge that I really enjoy coding on it. The code for this project is avaliable on this repository.

Time Scale Modification Algorithms

Sat, 11 Feb 2023 00:00:00 +0000

Time scale modifications algorithms are used to speed up or slow down the reproduction velocity of an audio. When you change the sample rate of an audio, the velocity changes but it also changes the pitch (when the audio is speed up it sounds highier pitch). There are different algorithms that change the velocity of the audio but mantain the pitch.

The main reference for this study is the following article, where all the different algorithms are describe in detail.

A Review of Time-Scale Modification of Music Signals.
— Jonathan Driedger and Meinard Müller¹

Algorithm comparison

There are two main algorithms, the Overlap-and-add (OLA) and the Phase Vocoder (PV). Both achieve good results under different signals and conditions. For this, a final implementation using Harmonic Percussion Separation (HPS) combines both algorithms and achiving the best results.

OLA

This method works on time domain and overlap sections of the audio (windows) and rearange it to achive a certain desire change on speed. This method works well for percussive signals, but it introduces artifacts when used with harmonic or tonal signals.

PV

This method works on frequency domain, and it combines chunks of audio in the frequency domain to achieve the desire change in time. This uses the phase vocoder principle to propagate the phase between the windows, this grantice the continuity of when applied to harmonic signals. On the contrary, it does not work for percussive signals becouse the phase propagation process eliminate the transients in the signals.

I created visualizations using Manim to enhance my class presentation. The first video demonstrates how the PV algorithm aligns windows to ensure smooth transitions in the generated signal over time. To achieve this, a Gaussian window is applied, which maintains continuity and smoothness, even at the start and end of the sequence.

The second video showcases the effects of applying the PV algorithm to a signal containing transients.

As predicted by theory, the transients vanish because the PV algorithm disrupts the vertical phase alignment. While these examples utilize idealized signals, they effectively demonstrate the key strengths and limitations of the algorithm.

HPS

To use both methods with their ideal signals, the HPS algorithms is used. This algorithm separete the signal into the harmonics and the percussive parts. It works by comparing the continuty of the signal in the STFT representation and using a filter to compare vertical versus horizontal presence on the spectrogram. With a threshold, a binary mask can be define over the spectrogram to separete the percussive parts from the harmoincs sections.

Results

We succesfully implement all the algorithms and compare them, verifying the theortical contents presented on the referenca article. In the process, we develope the toolkit to use this algorithms with the python programming language. All the code is avaliable on this repo

Academic Presentation

The study was preseented with my classmates on the JAAS (Jornadas de Acustica, Audio y Sonido). The main ideas and conclusions were preseneted on the conference. In the following repoert there is all the details and anlysis done for this proyect (in spanish).

ALGORITMOS DE MODIFICACIÓN DE ESCALA TEMPORAL.
— Matías Di Bernardo; Matías Vereertbruhggen; Sebastían Carro ²

A Review of Time-Scale Modification of Music Signal paper. ↩︎
JAAS 2023 - Algoritmos de Modificación de Escala Temporal paper. ↩︎

Comparative analysis of time-frequency transformations

Fri, 11 Nov 2022 00:00:00 +0000

This research is conducted as part of the subject Metodología de la Investigación at UNTREF. The article aims to compare the differences between three types of time-frequency transformations:

Fourier Transform (FT)
Wavelet Transform (WT)
Huang-Hilbert Transform (HHT)

The objective of this work is to understand the differences between these types of transformations and deepen my knowledge of signal processing.

Objective

The general objective of the research is to determine which spectral analysis tool achieves the highest accuracy in pitch detection tasks.

To achieve this objective, the following specific objectives are proposed:

Identify the parameters needed to represent the signal in the spectral domain for each case.
Select an algorithm that identifies the pitch of the signal based on its spectral representation.
Generate the data (audio signals) to be used for the comparison.
Evaluate the generated data with the different analysis methods and apply statistical processes to validate the results.
Establish a measure of accuracy for the pitch detection task.
Compare the results of the different analyses and determine which method achieves the highest accuracy in pitch detection.

The pitch detection task was chosen because it is one of the main applications of these types of transformations.

Algorithms

The theoretical analysis of all the transformations is performed in the continuous domain, but to conduct the experiments and comparisons, the discrete domain is used, enabling all calculations to be performed digitally.

FFT

The FFT is an algorithm that optimizes the DFT (Discrete Fourier Transform). With this algorithm, the spectral representation of the signal is obtained according to Fourier analysis, which decomposes a complex signal into a sum of sines or cosines. The DFT is calculated using the formula:

$$ X_k = \sum_{n=0}^{N-1} e^{-i\frac{2\pi}{N}kn} x_n $$

Where $ N $ is the number of signal samples, and $ k $ are natural numbers from $ 0 $ to $ N – 1 $.

WT

The Wavelet Transform (WT) uses an oscillatory function (wavelet) and applies a convolution between the signal and the chosen wavelet function to determine whether that wave shape is present in the signal. The wavelet is stretched and scaled in frequency and amplitude, allowing a single wavelet function to recreate the entire spectrum of interest.

In this research, the CDWT (Cyclic Discrete Wavelet Transform) will be used, the most common implementation when discretizing the WT. Conceptually, this transform extends Fourier analysis by projecting the signal onto a basis of wavelet functions instead of sines and cosines. It is calculated as follows:

$$ Wf[n, a^j] = \sum_{m=0}^{N-1} f[m] \psi_j[m-n] $$

Where $ N $ is the number of signal samples, $ \psi $ is the wavelet function, and $ j $ represents the deformation of the wavelet according to the selected wavelet bank.

HHT

Lastly, the Huang-Hilbert Transform (HHT) will be used for spectral representation. It employs a method called Empirical Mode Decomposition (EMD) to decompose the signal into subsignals that contain the relevant information of the original function.

Like the previous analysis methods, the key part of the analysis is the decomposition of the signal into simpler signals. However, instead of sine or wavelet functions, EMD finds intrinsic mode functions (IMFs) that form the basis of our decomposition and are unique to each signal.

The relationship between the IMFs and the frequency of the original signal is established with the equation:

$$ z(t) = f(t) + i H\{ f(t) \} $$

Where $ f(t) $ is an IMF of the original signal, and $ H $ is the Hilbert Transform. This allows the IMF to be represented as a complex signal by projecting it onto the imaginary axis using the Hilbert Transform.

Thus, the IMF is represented as a complex signal, and the amplitude and phase of each moment can be extracted to construct the spectral representation. Since a signal generally has multiple IMFs, this process is repeated for all of them, and the results are summed to obtain the complete spectrum.

Procedure

This research analyzes the relationship between types of spectral representation and accuracy in pitch detection.

First, the parameters for the different transformations will be selected. Among the most critical parameters to determine is the number of samples for temporal windowing, as it determines the trade-off between temporal and frequency resolution.

To ensure a fair comparison among the methods, data representing various cases of interest will be generated, including four types of signals:

Monophonic: Signals with a single note corresponding to $ F_0 $.
Polyphonic: Signals with multiple notes where harmony determines $ F_0 $.
Slow Transitions: Signals with gradual changes in $ F_0 $.
Fast Transitions: Signals with abrupt changes in $ F_0 $.

The real value $ V(t) $ will be compared to the result $ P(t) $ from each transform, integrating the difference over time to calculate the accuracy.

Results

At this stage, the task was to complete the research plan detailing the procedure and analysis methods. Dummy data was generated and statistically validated to simulate expected results.

The graph compares the precision achieved by the three transformations for different signal types. Based on the properties of the transforms, the Wavelet Transform (WT) is expected to outperform the Fourier Transform (FT), and the Huang-Hilbert Transform (HHT) is expected to achieve the highest precision overall.

Conclusions

In pitch detection tasks using spectral analysis, the Huang-Hilbert Transform (HHT) generally provides higher precision than the Fast Fourier Transform (FFT) and the Cyclic Discrete Wavelet Transform (CDWT).

The significance of this precision gain depends on the type of signal being analyzed, with fast-transition signals benefiting the least from the transformation change, while polyphonic signals show the most significant improvement when using the HHT.

This project allowed me to deepen my understanding of signal processing and grasp the foundations of why tools like the WT and HHT are used based on the characteristics of the signal being analyzed.

All details of this work are available in the following report.

Theather Acoustic Design

Wed, 22 Jun 2022 00:00:00 +0000

This project is the final assignment for the class Acoustics and Psychoacoustics II, where we were tasked with redesigning an existing auditorium. The goal was to apply the theory covered in class to create an acoustically optimized auditorium. For our project, we chose to redesign the Royal Albert Hall in London. This was particularly challenging due to the auditorium’s vast dimensions, which make it difficult to ensure that sound reaches all spectators equally.

Redesign Main Ideas

The redesign aimed to preserve the original concept of the auditorium, including its large volume and extensive seating capacity, while introducing critical changes to improve its acoustics. Although the primary focus was on acoustic enhancement, the redesign also considered other essential factors, such as sightlines and appropriate seat distribution.

Despite the intent to maintain the auditorium’s original dimensions, its volume proved too large to achieve an optimal reverberation time. To address this, the redesign introduced an intermediate ceiling to reduce the spherical ceiling’s volume, and the main seating area was reduced. These changes helped create a better reverberation time in the room, as illustrated in the cross-section below.

Building Details and Regulations

To ensure a feasible and functional redesign, the following key aspects were carefully considered:

Seat distribution
Corridor spacing
Sightline optimization
Stage comfort

Acoustic Treatment

Acoustic treatment was the most critical part of this study and focused on two main aspects: reflections and reverberation time.

Reflections

Analyzing reflections is essential for the audience’s acoustic experience. The original Royal Albert Hall features a spherical ceiling that centralizes reflections, creating undesirable acoustic effects. To mitigate this, the redesign incorporated an intermediate ceiling with a specific geometry designed to distribute reflections evenly across the audience.

The staggered ceiling design ensures adequate reflections for all seating rows. In the main balcony, two reflections were specifically addressed to compensate for the lower sound pressure level (SPL) caused by the large distance from the stage, as shown in the image below.

Lateral reflections were also optimized through adjustments to the stage geometry and the walls of the lateral balconies.

Additionally, the redesign sought to minimize the Initial Time Delay Gap (ITDG) across different audience locations.

Materials and Reverberation Time

The redesign adhered to recommendations from Acoustic Absorbers and Diffusers to achieve a balance between absorption, diffusion, and specular reflections. Reflective materials were used for the ceiling and parts of the lateral balconies to ensure effective specular reflections. To lower the reverberation time (RT), materials with higher absorption coefficients were applied to other surfaces.

Using the selected materials and the Sabine equation, we calculated the auditorium’s estimated RT. The resulting reverberation time for different frequencies is shown below:

The calculated mid-frequency RT is 2.51 seconds. While this is slightly above the recommended maximum of 2.4 seconds for optimal acoustics, it is acceptable given the auditorium’s large volume.

3D Modelling

We rendered the redesigned auditorium using SketchUp software. Below are some of the visualizations:

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Conclusions

Redesigning the Royal Albert Hall to improve its acoustics while retaining its original essence presented significant challenges. The project required innovative solutions to address acoustic issues without compromising the hall’s iconic design. Although some changes were necessary, the final result demonstrates a thoughtful redesign that enhances acoustics while preserving the auditorium’s historical character. This project also deepened our understanding of acoustics and auditorium design principles.

A detailed description of this project can be found in the following article.