The following is a sub-list of categories and functions currently supported by the scipy.signal package.  The scipy documentation breaks the signal package into the following categories:

• Convolution
• B-splines
• Filtering
• Filter design
• Continuous-time linear systems
• LTI representations
• Waveforms
• Window Functions
• Wavelets

A list of functions from the scipy.signal import:

'abcd_normalize', 'abs', 'absolute', 'add', 'allclose', 'any', 'arange',
'arccosh', 'arcsinh', 'arctan', 'arctan2', 'argsort', 'array', 'asarray',
'atleast_1d', 'atleast_2d', 'band_dict', 'band_stop_obj', 'barthann',
'bartlett', 'bessel', 'besselap', 'bilinear', 'blackman', 'blackmanharris',
'bohman', 'boxcar', 'bspline', 'bsplines', 'buttap', 'butter', 'buttord',
'c0_P', 'cascade', 'cast', 'ceil', 'cheb1ap', 'cheb1ord', 'cheb2ap',
'cheb2ord', 'chebwin', 'cheby1', 'cheby2', 'chirp', 'cmplx_sort', 'comb',
'conjugate', 'convolve', 'convolve2d', 'correlate', 'correlate2d', 'cos',
'cosh', 'cspline1d', 'cspline1d_eval', 'cspline2d', 'cubic', 'daub',
'decimate', 'deconvolve', 'detrend', 'diag', 'dot', 'ellip', 'ellipap',
'ellipord', 'exp', 'expand_dims', 'extract', 'eye', 'factorial', 'fft',
'fft2', 'fftconvolve', 'fftfreq', 'fftn', 'filter_design', 'filter_dict',
'filtfilt', 'findfreqs', 'fir_filter_design', 'firwin', 'firwin2', 'flattop',
'flipud', 'floor', 'freqs', 'freqz', 'gamma', 'gauss_spline', 'gaussian',
'gausspulse', 'general_gaussian', 'get_window', 'greater', 'greater_equal',
'hamming', 'hann', 'hanning', 'hilbert', 'hilbert2', 'ifft', 'ifft2', 'ifftn',
'ifftshift', 'iirdesign', 'iirfilter', 'impulse', 'impulse2', 'integrate',
'interpolate', 'invres', 'invresz', 'irfft', 'iscomplexobj', 'isscalar',
'kaiser', 'kaiser_atten', 'kaiser_beta', 'kaiserord', 'kratio', 'less',
'less_equal', 'lfilter', 'lfilter_zi', 'lfiltic', 'linalg', 'linspace',
'log', 'log10', 'logical_and', 'logspace', 'lp2bp', 'lp2bs', 'lp2hp', 'lp2lp',
'lsim', 'lsim2', 'lti', 'ltisys', 'maxflat', 'mean', 'medfilt', 'medfilt2d',
'mintypecode', 'mod', 'morlet', 'nan', 'nan_to_num', 'ndarray', 'newaxis',
'normalize', 'np', 'numpy', 'nuttall', 'ones', 'optimize', 'order_filter',
'parzen', 'pi', 'piecewise', 'place', 'poly', 'polyadd', 'polyder', 'polydiv',
'polyint', 'polymul', 'polysub', 'polyval', 'prod', 'product', 'qmf',
'qspline1d', 'qspline1d_eval', 'qspline2d', 'quadratic', 'r_', 'rank',
'ravel', 'real', 'real_if_close', 'remez', 'resample', 'reshape', 'residue',
'residuez', 'resize', 'roots', 'sawtooth', 'scipy', 'sepfir2d', 'signaltools',
'sigtools', 'sin', 'sinc', 'sinh', 'slepian', 'sort', 'special', 'spline',
'spline_filter', 'sqrt', 'square', 'squeeze', 'ss2tf', 'ss2zpk', 'step',
'step2', 'sum', 'sweep_poly', 'symiirorder1', 'symiirorder2', 'take', 'tan',
'test', 'tf2ss', 'tf2zpk', 'transpose', 'triang', 'types', 'unique',
'unique_roots', 'vratio', 'warnings', 'waveforms', 'wavelets', 'where',
'wiener', 'windows', 'yulewalk', 'zeros', 'zeros_like', 'zpk2ss', 'zpk2tf'

As seen in my other posts (blog1, blog2, snip1, snip2, snip3) I use Python quite extensively and the scipy.signal package.  I have found Python plus the numerical / scientific packages to be very powerful and productive.

As mentioned, if you enjoy signal processing and open-source packages getting involved with scipy.signal could be a opportunity to gain experience with signal processing software and group development.  I plan on contributing but (excuses, excuses) my time is limited and I also contribute to MyHDL (and would like to contribute more).

Even if you do not want to or cannot contribute to the scipy.signal development try out the Python programming language and the signal processing packages available.