Comparison of Wideband Earpiece Integrations in Mobile Phone

Perinteisesti puhelinverkoissa välitettävä puhe on ollut kapeakaistaista, kaistan ollessa 300 - 3400 Hz. Voidaan kuitenkin olettaa, että laajakaistaiset puhepalvelut tulevat saamaan markkinoilla enemmän jalansijaa tulevina vuosina. Tässä lopputyössä esitellään puheenkoodauksen perusteet laajakaistaisen adaptiivisen moninopeuspuhekoodekin (AMR-WB) kanssa. Laajakaistainen puhekoodekki laajentaa puhekaistan 50-7000 Hz käyttäen 16 kHz näytetaajuutta. Käytännössä laajempi kaista tarkoittaa parannuksia puheen ymmärrettävyyteen ja tekee siitä luonnollisemman ja mukavamman kuuloista. Tämän lopputyön päätavoite on vertailla kahden eri laajakaistaisen matkapuhelinkuulokkeen integrointia. Kysymys kuuluu, kuinka paljon käyttäjä hyötyy isommasta kuulokkeesta matkapuhelimessa? Kuulokkeiden suorituskyvyn selvittämiseksi niille tehtiin objektiivisia mittauksia vapaakentässä. Mittauksia tehtiin myös puhelimelle pää- ja torsosimulaattorissa (HATS) johdottamalla kuuloke suoraan vahvistimelle, sekä lisäksi puhelun ollessa aktiivisena GSM ja WCDMA verkoissa. Objektiiviset mittaukset osoittivat kahden eri integroinnin väliset erot kuulokkeiden taajuusvasteessa ja särössä erityisesti matalilla taajuuksilla. Lopuksi tehtiin kuuntelukoe tarkoituksena selvittää erottaako loppukäyttäjä pienemmän ja isomman kuulokkeen välistä eroa käyttäen kapeakaistaisia ja laajakaistaisia puhelinääninäytteitä. Kuuntelukokeen tuloksien pohjalta voidaan sanoa, että käyttäjä erottaa kahden eri integroinnin erot ja miespuhuja hyötyy naispuhujaa enemmän isommasta kuulokkeesta laajakaistaisella puhekoodekilla.The speech in telecommunication networks has been traditionally narrowband ranging from 300 Hz to 3400 Hz. It can be expected that wideband speech call services will increase their foothold in the markets during the coming years. In this thesis speech coding basics with adaptive multirate wideband (AMR-WB) are introduced. The wideband codec widens the speech band to new range from 50 Hz to 7000 Hz using 16 kHz sampling frequency. In practice the wider band means improvements to speech intelligibility and makes it more natural and comfortable to listen to. The main focus of this thesis work is to compare two different wideband earpiece integrations. The question is how much the end-user will benefit from using a larger earpiece in a mobile phone? To find out speaker performance, objective measurements in free field were done for the earpiece modules. Measurements were performed also for the phone on head and torso simulator (HATS) by wiring the earpieces directly to a power amplifier and with over the air on GSM and WCDMA networks. The results of objective measurements showed differences between the earpiece integrations especially on low frequencies in frequency response and distortion. Finally the subjective listening test is done for comparison to see if the end-user notices the difference between smaller and larger earpiece integrations using narrowband and wideband speech samples. Based on these subjective test results it can be said that the user can differentiate between two different integrations and that a male speaker benefits more from a larger earpiece than a female speaker

Structured Compressed Sensing: From Theory to Applications

Compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous review articles in CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity. In recent years, CS has worked its way into several new application areas. This, in turn, necessitates a fresh look on many of the basics of CS. The random matrix measurement operator must be replaced by more structured sensing architectures that correspond to the characteristics of feasible acquisition hardware. The standard sparsity prior has to be extended to include a much richer class of signals and to encode broader data models, including continuous-time signals. In our overview, the theme is exploiting signal and measurement structure in compressive sensing. The prime focus is bridging theory and practice; that is, to pinpoint the potential of structured CS strategies to emerge from the math to the hardware. Our summary highlights new directions as well as relations to more traditional CS, with the hope of serving both as a review to practitioners wanting to join this emerging field, and as a reference for researchers that attempts to put some of the existing ideas in perspective of practical applications.Comment: To appear as an overview paper in IEEE Transactions on Signal Processin

Advancements of MultiRate Signal processing for Wireless Communication Networks: Current State Of the Art

With the hasty growth of internet contact and voice and information centric communications, many contact technologies have been urbanized to meet the stringent insist of high speed information transmission and viaduct the wide bandwidth gap among ever-increasing high-data-rate core system and bandwidth-hungry end-user complex. To make efficient consumption of the limited bandwidth of obtainable access routes and cope with the difficult channel environment, several standards have been projected for a variety of broadband access scheme over different access situation (twisted pairs, coaxial cables, optical fibers, and unchanging or mobile wireless admittance). These access situations may create dissimilar channel impairments and utter unique sets of signal dispensation algorithms and techniques to combat precise impairments. In the intended and implementation sphere of those systems, many research issues arise. In this paper we present advancements of multi-rate indication processing methodologies that are aggravated by this design trend. The thesis covers the contemporary confirmation of the current literature on intrusion suppression using multi-rate indication in wireless communiquE9; networks

Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals

Wideband analog signals push contemporary analog-to-digital conversion systems to their performance limits. In many applications, however, sampling at the Nyquist rate is inefficient because the signals of interest contain only a small number of significant frequencies relative to the bandlimit, although the locations of the frequencies may not be known a priori. For this type of sparse signal, other sampling strategies are possible. This paper describes a new type of data acquisition system, called a random demodulator, that is constructed from robust, readily available components. Let K denote the total number of frequencies in the signal, and let W denote its bandlimit in Hz. Simulations suggest that the random demodulator requires just O(K log(W/K)) samples per second to stably reconstruct the signal. This sampling rate is exponentially lower than the Nyquist rate of W Hz. In contrast with Nyquist sampling, one must use nonlinear methods, such as convex programming, to recover the signal from the samples taken by the random demodulator. This paper provides a detailed theoretical analysis of the system's performance that supports the empirical observations.Comment: 24 pages, 8 figure

Compressive Sensing of Analog Signals Using Discrete Prolate Spheroidal Sequences

Compressive sensing (CS) has recently emerged as a framework for efficiently capturing signals that are sparse or compressible in an appropriate basis. While often motivated as an alternative to Nyquist-rate sampling, there remains a gap between the discrete, finite-dimensional CS framework and the problem of acquiring a continuous-time signal. In this paper, we attempt to bridge this gap by exploiting the Discrete Prolate Spheroidal Sequences (DPSS's), a collection of functions that trace back to the seminal work by Slepian, Landau, and Pollack on the effects of time-limiting and bandlimiting operations. DPSS's form a highly efficient basis for sampled bandlimited functions; by modulating and merging DPSS bases, we obtain a dictionary that offers high-quality sparse approximations for most sampled multiband signals. This multiband modulated DPSS dictionary can be readily incorporated into the CS framework. We provide theoretical guarantees and practical insight into the use of this dictionary for recovery of sampled multiband signals from compressive measurements