Audio source separation for music in low-latency and high-latency scenarios

Aquesta tesi proposa mètodes per tractar les limitacions de les tècniques existents de separació de fonts musicals en condicions de baixa i alta latència. En primer lloc, ens centrem en els mètodes amb un baix cost computacional i baixa latència. Proposem l'ús de la regularització de Tikhonov com a mètode de descomposició de l'espectre en el context de baixa latència. El comparem amb les tècniques existents en tasques d'estimació i seguiment dels tons, que són passos crucials en molts mètodes de separació. A continuació utilitzem i avaluem el mètode de descomposició de l'espectre en tasques de separació de veu cantada, baix i percussió. En segon lloc, proposem diversos mètodes d'alta latència que milloren la separació de la veu cantada, gràcies al modelatge de components específics, com la respiració i les consonants. Finalment, explorem l'ús de correlacions temporals i anotacions manuals per millorar la separació dels instruments de percussió i dels senyals musicals polifònics complexes.Esta tesis propone métodos para tratar las limitaciones de las técnicas existentes de separación de fuentes musicales en condiciones de baja y alta latencia. En primer lugar, nos centramos en los métodos con un bajo coste computacional y baja latencia. Proponemos el uso de la regularización de Tikhonov como método de descomposición del espectro en el contexto de baja latencia. Lo comparamos con las técnicas existentes en tareas de estimación y seguimiento de los tonos, que son pasos cruciales en muchos métodos de separación. A continuación utilizamos y evaluamos el método de descomposición del espectro en tareas de separación de voz cantada, bajo y percusión. En segundo lugar, proponemos varios métodos de alta latencia que mejoran la separación de la voz cantada, gracias al modelado de componentes que a menudo no se toman en cuenta, como la respiración y las consonantes. Finalmente, exploramos el uso de correlaciones temporales y anotaciones manuales para mejorar la separación de los instrumentos de percusión y señales musicales polifónicas complejas.This thesis proposes specific methods to address the limitations of current music source separation methods in low-latency and high-latency scenarios. First, we focus on methods with low computational cost and low latency. We propose the use of Tikhonov regularization as a method for spectrum decomposition in the low-latency context. We compare it to existing techniques in pitch estimation and tracking tasks, crucial steps in many separation methods. We then use the proposed spectrum decomposition method in low-latency separation tasks targeting singing voice, bass and drums. Second, we propose several high-latency methods that improve the separation of singing voice by modeling components that are often not accounted for, such as breathiness and consonants. Finally, we explore using temporal correlations and human annotations to enhance the separation of drums and complex polyphonic music signals

Signal Processing Methods for Music Synchronization, Audio Matching, and Source Separation

The field of music information retrieval (MIR) aims at developing techniques and tools for organizing, understanding, and searching multimodal information in large music collections in a robust, efficient and intelligent manner. In this context, this thesis presents novel, content-based methods for music synchronization, audio matching, and source separation. In general, music synchronization denotes a procedure which, for a given position in one representation of a piece of music, determines the corresponding position within another representation. Here, the thesis presents three complementary synchronization approaches, which improve upon previous methods in terms of robustness, reliability, and accuracy. The first approach employs a late-fusion strategy based on multiple, conceptually different alignment techniques to identify those music passages that allow for reliable alignment results. The second approach is based on the idea of employing musical structure analysis methods in the context of synchronization to derive reliable synchronization results even in the presence of structural differences between the versions to be aligned. Finally, the third approach employs several complementary strategies for increasing the accuracy and time resolution of synchronization results. Given a short query audio clip, the goal of audio matching is to automatically retrieve all musically similar excerpts in different versions and arrangements of the same underlying piece of music. In this context, chroma-based audio features are a well-established tool as they possess a high degree of invariance to variations in timbre. This thesis describes a novel procedure for making chroma features even more robust to changes in timbre while keeping their discriminative power. Here, the idea is to identify and discard timbre-related information using techniques inspired by the well-known MFCC features, which are usually employed in speech processing. Given a monaural music recording, the goal of source separation is to extract musically meaningful sound sources corresponding, for example, to a melody, an instrument, or a drum track from the recording. To facilitate this complex task, one can exploit additional information provided by a musical score. Based on this idea, this thesis presents two novel, conceptually different approaches to source separation. Using score information provided by a given MIDI file, the first approach employs a parametric model to describe a given audio recording of a piece of music. The resulting model is then used to extract sound sources as specified by the score. As a computationally less demanding and easier to implement alternative, the second approach employs the additional score information to guide a decomposition based on non-negative matrix factorization (NMF)

A Survey on Generative Diffusion Model

Deep learning shows excellent potential in generation tasks thanks to deep latent representation. Generative models are classes of models that can generate observations randomly concerning certain implied parameters. Recently, the diffusion Model has become a rising class of generative models by its power-generating ability. Nowadays, great achievements have been reached. More applications except for computer vision, speech generation, bioinformatics, and natural language processing are to be explored in this field. However, the diffusion model has its genuine drawback of a slow generation process, single data types, low likelihood, and the inability for dimension reduction. They are leading to many enhanced works. This survey makes a summary of the field of the diffusion model. We first state the main problem with two landmark works -- DDPM and DSM, and a unified landmark work -- Score SDE. Then, we present improved techniques for existing problems in the diffusion-based model field, including speed-up improvement For model speed-up improvement, data structure diversification, likelihood optimization, and dimension reduction. Regarding existing models, we also provide a benchmark of FID score, IS, and NLL according to specific NFE. Moreover, applications with diffusion models are introduced including computer vision, sequence modeling, audio, and AI for science. Finally, there is a summarization of this field together with limitations \& further directions. The summation of existing well-classified methods is in our Github:https://github.com/chq1155/A-Survey-on-Generative-Diffusion-Model

Networks of Liveness in Singer-Songwriting: A practice-based enquiry into developing audio-visual interactive systems and creative strategies for composition and performance.

This enquiry explores the creation and use of computer-based, real-time interactive audio-visual systems for the composition and performance of popular music by solo artists. Using a practice-based methodology, research questions are identified that relate to the impact of incorporating interactive systems into the songwriting process and the liveness of the performances with them. Four approaches to the creation of interactive systems are identified: creating explorative-generative tools, multiple tools for guitar/vocal pieces, typing systems and audio-visual metaphors. A portfolio of ten pieces that use these approaches was developed for live performance. A model of the songwriting process is presented that incorporates system-building and strategies are identified for reconciling the indeterminate, electronic audio output of the system with composed popular music features and instrumental/vocal output. The four system approaches and ten pieces are compared in terms of four aspects of liveness, derived from current theories. It was found that, in terms of overall liveness, a unity to system design facilitated both technological and aesthetic connections between the composition, the system processes and the audio and visual outputs. However, there was considerable variation between the four system approaches in terms of the different aspects of liveness. The enquiry concludes by identifying strategies for maximising liveness in the different system approaches and discussing the connections between liveness and the songwriting process

Pitch-Informed Solo and Accompaniment Separation

Das Thema dieser Dissertation ist die Entwicklung eines Systems zur Tonhöhen-informierten Quellentrennung von Musiksignalen in Soloinstrument und Begleitung. Dieses ist geeignet, die dominanten Instrumente aus einem Musikstück zu isolieren, unabhängig von der Art des Instruments, der Begleitung und Stilrichtung. Dabei werden nur einstimmige Melodieinstrumente in Betracht gezogen. Die Musikaufnahmen liegen monaural vor, es kann also keine zusätzliche Information aus der Verteilung der Instrumente im Stereo-Panorama gewonnen werden. Die entwickelte Methode nutzt Tonhöhen-Information als Basis für eine sinusoidale Modellierung der spektralen Eigenschaften des Soloinstruments aus dem Musikmischsignal. Anstatt die spektralen Informationen pro Frame zu bestimmen, werden in der vorgeschlagenen Methode Tonobjekte für die Separation genutzt. Tonobjekt-basierte Verarbeitung ermöglicht es, zusätzlich die Notenanfänge zu verfeinern, transiente Artefakte zu reduzieren, gemeinsame Amplitudenmodulation (Common Amplitude Modulation CAM) einzubeziehen und besser nichtharmonische Elemente der Töne abzuschätzen. Der vorgestellte Algorithmus zur Quellentrennung von Soloinstrument und Begleitung ermöglicht eine Echtzeitverarbeitung und ist somit relevant für den praktischen Einsatz. Ein Experiment zur besseren Modellierung der Zusammenhänge zwischen Magnitude, Phase und Feinfrequenz von isolierten Instrumententönen wurde durchgeführt. Als Ergebnis konnte die Kontinuität der zeitlichen Einhüllenden, die Inharmonizität bestimmter Musikinstrumente und die Auswertung des Phasenfortschritts für die vorgestellte Methode ausgenutzt werden. Zusätzlich wurde ein Algorithmus für die Quellentrennung in perkussive und harmonische Signalanteile auf Basis des Phasenfortschritts entwickelt. Dieser erreicht ein verbesserte perzeptuelle Qualität der harmonischen und perkussiven Signale gegenüber vergleichbaren Methoden nach dem Stand der Technik. Die vorgestellte Methode zur Klangquellentrennung in Soloinstrument und Begleitung wurde zu den Evaluationskampagnen SiSEC 2011 und SiSEC 2013 eingereicht. Dort konnten vergleichbare Ergebnisse im Hinblick auf perzeptuelle Bewertungsmaße erzielt werden. Die Qualität eines Referenzalgorithmus im Hinblick auf den in dieser Dissertation beschriebenen Instrumentaldatensatz übertroffen werden. Als ein Anwendungsszenario für die Klangquellentrennung in Solo und Begleitung wurde ein Hörtest durchgeführt, der die Qualitätsanforderungen an Quellentrennung im Kontext von Musiklernsoftware bewerten sollte. Die Ergebnisse dieses Hörtests zeigen, dass die Solo- und Begleitspur gemäß unterschiedlicher Qualitätskriterien getrennt werden sollten. Die Musiklernsoftware Songs2See integriert die vorgestellte Klangquellentrennung bereits in einer kommerziell erhältlichen Anwendung.This thesis addresses the development of a system for pitch-informed solo and accompaniment separation capable of separating main instruments from music accompaniment regardless of the musical genre of the track, or type of music accompaniment. For the solo instrument, only pitched monophonic instruments were considered in a single-channel scenario where no panning or spatial location information is available. In the proposed method, pitch information is used as an initial stage of a sinusoidal modeling approach that attempts to estimate the spectral information of the solo instrument from a given audio mixture. Instead of estimating the solo instrument on a frame by frame basis, the proposed method gathers information of tone objects to perform separation. Tone-based processing allowed the inclusion of novel processing stages for attack refinement, transient interference reduction, common amplitude modulation (CAM) of tone objects, and for better estimation of non-harmonic elements that can occur in musical instrument tones. The proposed solo and accompaniment algorithm is an efficient method suitable for real-world applications. A study was conducted to better model magnitude, frequency, and phase of isolated musical instrument tones. As a result of this study, temporal envelope smoothness, inharmonicty of musical instruments, and phase expectation were exploited in the proposed separation method. Additionally, an algorithm for harmonic/percussive separation based on phase expectation was proposed. The algorithm shows improved perceptual quality with respect to state-of-the-art methods for harmonic/percussive separation. The proposed solo and accompaniment method obtained perceptual quality scores comparable to other state-of-the-art algorithms under the SiSEC 2011 and SiSEC 2013 campaigns, and outperformed the comparison algorithm on the instrumental dataset described in this thesis.As a use-case of solo and accompaniment separation, a listening test procedure was conducted to assess separation quality requirements in the context of music education. Results from the listening test showed that solo and accompaniment tracks should be optimized differently to suit quality requirements of music education. The Songs2See application was presented as commercial music learning software which includes the proposed solo and accompaniment separation method

Single Channel auditory source separation with neural network

Although distinguishing different sounds in noisy environment is a relative easy task for human, source separation has long been extremely difficult in audio signal processing. The problem is challenging for three reasons: the large variety of sound type, the abundant mixing conditions and the unclear mechanism to distinguish sources, especially for similar sounds. In recent years, the neural network based methods achieved impressive successes in various problems, including the speech enhancement, where the task is to separate the clean speech out of the noise mixture. However, the current deep learning based source separator does not perform well on real recorded noisy speech, and more importantly, is not applicable in a more general source separation scenario such as overlapped speech. In this thesis, we firstly propose extensions for the current mask learning network, for the problem of speech enhancement, to fix the scale mismatch problem which is usually occurred in real recording audio. We solve this problem by combining two additional restoration layers in the existing mask learning network. We also proposed a residual learning architecture for the speech enhancement, further improving the network generalization under different recording conditions. We evaluate the proposed speech enhancement models on CHiME 3 data. Without retraining the acoustic model, the best bi-direction LSTM with residue connections yields 25.13% relative WER reduction on real data and 34.03% WER on simulated data. Then we propose a novel neural network based model called “deep clustering” for more general source separation tasks. We train a deep network to assign contrastive embedding vectors to each time-frequency region of the spectrogram in order to implicitly predict the segmentation labels of the target spectrogram from the input mixtures. This yields a deep network-based analogue to spectral clustering, in that the embeddings form a low-rank pairwise affinity matrix that approximates the ideal affinity matrix, while enabling much faster performance. At test time, the clustering step “decodes” the segmentation implicit in the embeddings by optimizing K-means with respect to the unknown assignments. Experiments on single channel mixtures from multiple speakers show that a speaker-independent model trained on two-speaker and three speakers mixtures can improve signal quality for mixtures of held-out speakers by an average over 10dB. We then propose an extension for deep clustering named “deep attractor” network that allows the system to perform efficient end-to-end training. In the proposed model, attractor points for each source are firstly created the acoustic signals which pull together the time-frequency bins corresponding to each source by finding the centroids of the sources in the embedding space, which are subsequently used to determine the similarity of each bin in the mixture to each source. The network is then trained to minimize the reconstruction error of each source by optimizing the embeddings. We showed that this frame work can achieve even better results. Lastly, we introduce two applications of the proposed models, in singing voice separation and the smart hearing aid device. For the former, a multi-task architecture is proposed, which combines the deep clustering and the classification based network. And a new state of the art separation result was achieved, where the signal to noise ratio was improved by 11.1dB on music and 7.9dB on singing voice. In the application of smart hearing aid device, we combine the neural decoding with the separation network. The system firstly decodes the user’s attention, which is further used to guide the separator for the targeting source. Both objective study and subjective study show the proposed system can accurately decode the attention and significantly improve the user experience

Conservation of Limited Resources: Design Principles for Security and Usability on Mobile Devices

Mobile devices have evolved from an accessory to the primary computing device for an increasing portion of the general population. Not only is mobile the primary device, consumers on average have multiple Internet-connected devices. The trend towards mobile has resulted in a shift to “mobile-first” strategies for delivering information and services in business organizations, universities, and government agencies. Though principles for good security design exist, those principles were formulated based upon the traditional workstation configuration instead of the mobile platform. Security design needs to follow the shift to a “mobile-first” emphasis to ensure the usability of the security interface. The mobile platform has constraints on resources that can adversely impact the usability of security. This research sought to identify design principles for usable security for mobile devices that address the constraints of the mobile platform. Security and usability have been seen as mutually exclusive. To accurately identify design principles, the relationship between principles for good security design and usability design must be understood. The constraints for the mobile environment must also be identified, and then evaluated for their impact on the interaction of a consumer with a security interface. To understand how the application of the proposed mobile security design principles is perceived by users, an artifact was built to instantiate the principles. Through a series of guided interactions, the importance of proposed design principles was measured in a simulation, in human-computer interaction, and in user perception. The measures showed a resounding difference between the usability of the same security design delivered on mobile vs. workstation platform. It also reveals that acknowledging the constraints of an environment and compensating for the constraints yields mobile security that is both usable and secure. Finally, the hidden cost of security design choices that distract the user from the surrounding environment were examined from both the security perspective and public safety perspective

Machine Learning Approaches to Historic Music Restoration

In 1889, a representative of Thomas Edison recorded Johannes Brahms playing a piano arrangement of his piece titled “Hungarian Dance No. 1”. This recording acts as a window into how musical masters played in the 19th century. Yet, due to years of damage on the original recording medium of a wax cylinder, it was un-listenable by the time it was digitized into WAV format. This thesis presents machine learning approaches to an audio restoration system for historic music, which aims to convert this poor-quality Brahms piano recording into a higher quality one. Digital signal processing is paired with two machine learning approaches: non-negative matrix factorization and deep neural networks. Our results show the advantages and disadvantages of our approaches, when we compare them to a benchmark restoration of the same recording made by the Center for Computer Research in Music and Acoustics at Stanford University. They also show how this system provides the restoration potential for a wide range of historic music artifacts like this recording, requiring minimal overhead made possible by machine learning. Finally, we go into possible future improvements to these approaches